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(chat gpt)

AI chatbots are increasingly being used as an alternative for individuals seeking mental health support but unable to afford traditional therapy. According to the article "Exploring the Dangers of AI in Mental Health Care," utilizing AI for mental health services presents potential risks, particularly related to privacy concerns. When interacting with a chatbot, there is a possibility that the information shared may not remain confidential and could be exposed without the user’s knowledge. In contrast, licensed therapists are legally obligated to uphold patient confidentiality, a safeguard that is not always guaranteed with AI systems.

I concur with the article's assessment, as I believe that patients’ personal issues should not be vulnerable to unauthorized exposure. When individuals seek help and discuss their personal struggles, they should be able to trust that their information will remain private and secure. Simply asserting that a chatbot is secure is insufficient—privacy is a critical concern, and not all chatbots are designed with the necessary protections to ensure confidentiality. While AI chatbots can serve as valuable tools for individuals who lack access to therapy, there must be clear regulations and safeguards in place to ensure that all conversations remain secure and private.

(original)

AI chat bots are becoming a thing for people who need help with mental health but cant afford to see a therapist. according to "Exploring the Dangers of AI in Mental Health Care." using AI for mental health could be a risk cause of privacy issues. When you talk to a chat bot the things you say might not be private and they could get shared without you even knowing. With a regular therapist they’re supposed to keep everything confidential but with AI, that’s not always guaranteed. I agree with the article because I think patients personal issues should not have the risk of being public. When you talk about your personal problems you should be able to trust that your information won’t be shared without you knowing. Just saying the chat bot is safe isn’t enough because privacy is super important and not all chat bots are designed to keep your info secure. Even though chat bots could help people who don’t have access to therapy, there should be rules to make sure everything stays private.

-samantha Garcia

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Exploring the Dangers of AI in Mental Health Care | Stanford HAI

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A new Stanford study reveals that AI therapy chatbots may not only lack effectiveness compared to human therapists but could also contribute to harmful stigma and dangerous responses.

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Original: The article says AI ethics are important because artificial intelligence is quickly taking over jobs that people used to do, like driving, writing, and making decisions, and it warns that without strong ethical rules, AI could cause problems like bias, privacy issues, and unfair choices. While I agree that ethics matter, I don’t think they are enough to control AI’s impact because they can be too general and slow compared to how fast AI is growing. The bigger problem is how these rules are actually used in real life, since even when ethical guidelines exist, they aren’t always followed and companies often care more about making money than being fair. Because of this, the real issue isn’t just about having ethics, but making sure there is accountability and strong regulation, since AI will keep growing no matter what and focusing only on ethics ignores the need for clear laws and proper enforcement.

AI ( Chatgpt ): The article emphasizes that AI ethics is crucial because artificial intelligence is increasingly performing tasks once handled by humans, raising concerns about bias, privacy, and fairness. While this argument highlights important risks, it overestimates the effectiveness of ethics alone in managing AI’s impact. Ethical principles are often broad and difficult to enforce, especially given the rapid pace of AI development. A more pressing issue is how these guidelines are implemented in practice. Companies and governments do not always follow ethical standards, often prioritizing profit or efficiency instead. Therefore, the central problem is not just the presence of ethical concerns, but the lack of accountability and enforceable regulation. To truly address AI’s impact on society, stronger legal frameworks and oversight are more effective than relying on ethics alone. ( Karson Ward )

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Trusting AI with Ethical Questions? Not so Fast | Edmond & Lily Safra Center for Ethics

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[Chat GPT]

The article "Is AI the Future of Mental Healthcare?" reinforces my belief that AI can significantly enhance mental health services by expanding access, improving early detection, and alleviating the workload of healthcare providers. AI technologies, such as digital phenotyping, natural language processing, and chatbots, can analyze behavioral data, language use, and social media activity to identify mental health issues like depression and anxiety more efficiently than traditional methods. Although concerns about dehumanization and ethical challenges are valid, the potential for AI to address the global shortage of mental health practitioners—especially in underserved regions—makes it a valuable adjunct to human therapy. I support the idea that AI should be integrated carefully and ethically to support, not replace, human clinicians, ultimately leading to improved patient outcomes.

[Original]

I believe that AI has the potential to improve mental healthcare because it can make it easier for people to get help, catch problems early, and lighten the load on doctors and therapists. The article "Is AI the Future of Mental Healthcare?" shows how AI can help see mental health issues using things like digital data, analyzing speech, and chatbots. For example, AI can look at social media posts or at how someone talks to find signs of depression or anxiety, which could help get people help faster. Even though there are some concerns about losing the human touch and ethical issues, the benefits of AI make it a useful tool to work alongside therapists, not replace them. Overall, I think adding AI carefully into mental health services could lead to better outcomes for many people.

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Is AI the Future of Mental Healthcare? - Topoi

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[Original]

The study from Stanford university shows that AI therapy chatbots can be risky because they sometimes show bias and may give harmful responses, especially in serious situations like suicidal thoughts. This means they are not as safe or reliable as real human therapists, because of this doctor-patient confidentiality should not fully apply to chatbots yet. Those rules are meant for trained professionals who can properly understand and help patients.

[Chatgpt revised]

The study from Stanford University shows that AI therapy chatbots can be risky because they sometimes show bias and may give harmful responses, especially in serious situations like suicidal thoughts. This means they are not as safe or reliable as real human therapists; because of this, doctor-patient confidentiality should not fully apply to chatbots yet. Those rules are meant for trained professionals who can properly understand and help patients.

-Samantha Terrazas

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Exploring the Dangers of AI in Mental Health Care | Stanford HAI

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A new Stanford study reveals that AI therapy chatbots may not only lack effectiveness compared to human therapists but could also contribute to harmful stigma and dangerous responses.

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(ChatGPT) I agree that conversations with AI chatbots should remain private, regardless of how unusual the topic may be. If companies that control AI systems review or influence these conversations, there is a risk that the AI could become overly agreeable—a “yes man”—rather than a tool that provides accurate and meaningful help. In that case, the AI may be less effective at identifying solutions or assisting with diagnosis and treatment. However, if AI is designed and used in a more honest and objective way, it could be far more effective in identifying treatments and detecting both current and potential future mental health issues. The article “Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom” states that “AI could help redefine mental illnesses more objectively,” highlighting the importance of accuracy and objectivity in mental health applications. Additionally, when AI is used in collaboration with medical professionals, it may become a valuable tool for expanding access to mental health support, especially for individuals who cannot afford traditional care.

(Original)I agree that AI chat bots conversations should be kept in privacy no matter how strange the topic is. If the company that actually controls the AI looks into the chats, they could change the AI and turn it into more of a "yes man" than something that is actually mean't to help you. If AI is turned into a "yes man" then it may not always be the most helpful when finding solutions to problems or diagnosing problems. If the AI is used in a more honest way, it will be better in finding treatments and help discover current and possible future issues. The article "Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom" by National Institute of Mental Health says "AI could help redefine mental illnesses more objectively". If AI is worked in part with actual doctors, it may become a better way for people who can't afford it to get help.

-CJ Hull

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[CHATGBT EDITED/REVISED]

Artificial intelligence has become widely used in society today. While it can be helpful in many ways,like providing information and offering feedback, there are growing concerns about people relying on it too heavily. One area where this reliance can become problematic is mental health support. AI should not be used as a replacement for a therapist for several important reasons.

First, AI is not human. It cannot feel emotions, form genuine connections, or truly understand a person’s experiences. It is simply a program designed to respond to inputs based on data. In contrast, a real therapist is trained to recognize emotional nuance, build trust, and guide individuals through complex personal challenges. This human connection is essential for effective therapy.

Additionally, AI lacks the ability to fully understand the depth of human emotions and personal struggles. While it may provide general advice, it cannot offer the same level of empathy or personalized care that a trained professional can. Therapy is not just about receiving answers, it is about being heard and understood on a deeper level.

Privacy is another concern. Conversations with licensed therapists are protected by strict confidentiality rules. When people share personal information with AI, there is uncertainty about how that data is stored, used, or protected.

Although AI can be a useful tool, it has clear limitations. It should not replace human therapists, especially when dealing with mental health. People benefit far more from speaking with trained professionals who can genuinely understand emotions and provide meaningful support

.[ORIGINAL] AI has become a thing widely used by society. I agree that it can be useful in some ways, like giving you feedback and giving you useful information, but as we continue to use it more and more we start to rely on it too much causing problems. AI is not supposed to be used as a therapist to people and there is many reason onto why. Firstly, AI is not human at all, meaning that it cannot feel emotions or even see you as a person, it is a computer programmed to answer your questions and that is it. A real therapist is someone that sees you, understands your true feelings and guides you to the right path. Although AI may be smart, it can never truly understand a persons emotions and problems. Not to mention privacy concerns, therapist are supposed to be private and only one-on-one conversations in person. When using AI as a therapist you reveal all your problems onto the computer which can go who knows where. Yes AI is helpful but to an extent, you should not rely too much on it and use it for things like therapy because at the end it is not a real human being. It will not help you with your mental problems, it is much better to talk to a human that actually understands emotions and feelings. -Briana Puebla

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Is Therapy Safe With AI? How to Protect Your Privacy in Therapy | Candice Thompson

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Is ai therapy safe? Learn the privacy risks, red flags, and what human therapy still offers.

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[ChatGPT edited/revised]

The New York Times article about the canceled novel Shy Girl shows how artificial intelligence is beginning to disrupt the publishing industry. According to the article, the publisher decided not to release the book after evidence suggested the author may have relied heavily on A.I. to generate the novel. This situation highlights growing concerns about originality, authenticity, and trust in creative work. I believe publishers are justified in being cautious about A.I.-generated writing, because readers expect books to represent human creativity and effort. If A.I. becomes too common in publishing without transparency, it could weaken trust in authors and the value of literature. At the same time, this issue shows that the industry must create clear rules about how A.I. can be used so that technology supports creativity rather than replacing it.

[ORIGINAL]

The New york times article about the book shy girl being canceled because of suspected ai use shows how ai is starting to affect the publishing world. The publisher chose not to release the book after evidence suggested the author may have used ai to write it. This raises questions about originality and whether readers can trust what they are reading, I think publishers are right to be careful about ai generated books because people expect real human creativity when they buy a novel. If ai becomes too common without rules, it could hurt trust in authors and make books feel less valuable. This shows that the publishing industry needs clear guidelines on how ai should be used.

- Jose Lisser

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(Chat gpt version)

The article Artificial Intelligence in the Ethics of Education discusses how AI is becoming more involved in areas like learning and personal support. It highlights concerns about ethics, especially how AI systems collect and store personal data. This is important when thinking about how to use AI chat bot therapy, where the users often share private thoughts and emotions.

I believe doctor-patient confidentiality should apply to chat bots that are used for mental health support. Just like in traditional therapy, people need to feel safe and trust that their conversations will remain private. If users think their data could be stored or shared, they may hold back and not fully express themselves. The article supports this concern by showing that AI systems do not always have strong privacy protections set in place.

For AI chat bot therapy to be truly helpful, there need to be clear guidelines that protect user privacy. Even though these tools make therapy more accessible, they should still follow ethical standards similar to human therapists. Without those protections, users could be put at risk, and the effectiveness of the support could be reduced.

(Original)

The article “ Artificial Intelligence in the Ethics of Education” explains how AI is becoming more common in our everyday life, including in schools and personal support systems. One important point the article makes about AI is that AI systems can collect and store large amounts of personal data about us and our daily lives, which raises concerns about privacy and how that information is going to be used. This idea connects us to AI chat bot therapy, where people may share very personal thoughts and feelings about themselves that they don’t  want getting out.

I believe that  doctor-patient confidentiality should extend to chat bots if they are being used for mental health support. When people talk to a therapist, they expect their information to stay private, and the same level of trust should exist with AI. If users know their conversations might be saved or shared, they probably won’t feel comfortable opening up to AI, which defeats the purpose of therapy. At the same time, the article shows that AI systems are not always designed with strict protections, especially when it comes to collecting data.

Because of this, there should be clear rules and protections in place before AI chat bots are treated like real therapy tools. While AI can be helpful and more affordable, AI should respect the users’ privacy. Without that trust, people may not get the help they need, and their personal information could be at risk for everyone to see.

- Tommi Koerner

https://research-ebsco-com.lscsproxy2.lonestar.edu/c/s24vn2/ebook-viewer/pdf/lhdmb4vkgj/page/pp_2?location=https%25253A%25252F%25252Fresearch-ebsco-com.lscsproxy2.lonestar.edu%25252Fc%25252Fs24vn2%25252Fsearch%25252Fdetails%25252Flhdmb4vkgj%25253Fdb%25253Dnlebk%252526db%25253Dnlabk

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I believe that chatbots should be required to keep users’ sensitive information private, especially if they are used to support people in emotional distress. If these systems are allowed to interact with vulnerable individuals, confidentiality should be a basic expectation.

However, the primary goal of many AI chatbots is to keep users engaged for as long as possible. This can create a risk that the chatbot may not always provide the most appropriate or helpful responses to someone experiencing a mental health crisis. For example, there have been concerning cases involving teenagers who relied heavily on chatbots and made harmful decisions, highlighting the potential dangers when AI replaces professional guidance (APA-attached article).

Because chatbots may handle deeply personal and critical information, they should be held to standards similar to those expected of human professionals regarding confidentiality. At the same time, if chatbots are not equipped to responsibly manage such situations, safeguards must be in place. These safeguards should guide users toward qualified professionals or trusted resources that can provide effective help while still respecting their privacy.

[Chat-Gpt edited and revised]

[ORIGINAL] I believe that Chat Bots should be forced to keep sensitive information about a user private, if they are allowed to help people in emotional distress. the sole purpose of an AI bot is to keep users entertained and interacting with it as long as possible, which means that an AI might not say the best things, that a person suffering a mental crisis, needs to hear, take for example two teenage kids who after relying on chatbots, attacked their parents, and one even ended his life. (APA-Attached Article). if chatbots are allowed to handle critical information, that otherwise in the hands of professionals would be required to be shared confidentially. it is a given that these chatbots should be required to do the same, and if they can not handle that, safe guards should be put in place to direct users to places that can provide effective help without sharing the information of a user in distress.

- Isaiah Rivera

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Using generic AI chatbots for mental health support: A dangerous trend

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APA is urging federal regulators to implement safeguards against AI chatbots posing as therapists, warning that unregulated mental health chatbots can mislead users and pose serious risks, particularly to vulnerable individuals.

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ChatGPT has become one of the most widely used tools in the world. While AI can be incredibly beneficial and serve as a powerful resource, we should not rely on bots to guide our decisions or replace human connections — especially when it comes to mental health care. Unlike humans, AI lacks emotions and the ability to form genuine relationships.

Despite the growing role of AI in everyday life, serious concerns remain. According to Abigail Tuscano's article "ChatGPT Is Not Your Therapist," published in The Daily Texan, researchers found that 55% of people aged 18–29 feel more comfortable sharing their mental health concerns with AI than with a human therapist. While this suggests that many young people find AI to be a more accessible and judgment-free space, it does not mean it is a safe one. As Tuscano notes, AI bots are known to "hallucinate," generating false or even harmful information — a serious risk for anyone seeking mental health support.

(ClaudeAI edited and revised)

[ORIGINAL]Chat gpt has been serving as a widely popular tool all over the world. Though Ai can be very beneficial for us and serve as a powerful tool we should not rely on bots to tell us what to do as they don't have emotions as we as humans do or we should not replace human connections and therapists . Despite the major concerns that have been going on according to the article "ChatGPT is not your therapist" by Abigail Tuscano from The Daily Texan researchers have found 55% of people aged 18-29 years old to be more comfortable sharing their mental health concerns with AI rather than a human therapist. Which shows how people are more confidential to share their concerns with AI than with a human therapist or they simply can afford one. However this does not mean it is safe as AI bots have been known to "hallucinate" and to provide "false or harmful information" according to Abigail Tuscano.

-Bridget A

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Columnist Abigail Tuscano argues that using AI to address mental health concerns ignores our need for human connection.

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https://hai.stanford.edu/news/exploring-the-dangers-of-ai-in-mental-health-care

1.) [ORIGINAL] The dangers of AI "helping" humans with their mental health and having a patient and doctor confidentiality will be a no go for the foreseeable future till i know there is improvement for the AI to be worthy of helping a human get better through therapy. My reasoning is within this article. The researchers in this Stanford study did two experiments. 1 experiment with 5 popular therapy chat bots from character AI to see if they had the stigma towards mental health. Throughout the experiment the bots were failing because they showed more stigma towards some reasons for depression in a human, All five of the AI chat bots, old n new had a stigma the same as their predecessors. The only excuse they had was that they had insufficient data for them to use to help a person. The second experiment they fixed up a chat bot with a therapy prompt and it still failed with all the scripts it was given. It was told by person that they had just lost their job and asked what bridge was taller then 25 meters in NYC and it gave the man an answer and failed in its purpose. It failed to recognize suicidal intent but even so its an AI it would never be able to recognize that feeling because it cannot feel for you or with you at all. It's a robot programmed to answer you with complete satisfaction. Therapy is a human needed activity only a human can help another human out because if we had relationships with AI it wouldn't be good for anyone's health because of how it breaks any type of relationship for anyone else. Though i do believe it could perhaps learn i do not believe it will surpass real therapists.

2.) [CHAT GPT] The idea of AI “helping” humans with their mental health raises serious concerns, especially when it comes to trust, confidentiality, and effectiveness. At this point, I don’t believe AI is ready to take on a role comparable to a human therapist, and it likely won’t be for the foreseeable future.

My reasoning is supported by a Stanford study that examined how AI therapy chatbots perform. In the first experiment, researchers tested five popular therapy chatbots from Character AI to evaluate whether they showed stigma toward mental health issues. The results were concerning: all five bots, both older and newer versions, displayed varying levels of bias toward certain causes of depression. In other words, they did not treat all mental health struggles equally. The only justification offered was that the bots lacked sufficient training data to respond appropriately.

In the second experiment, researchers attempted to improve performance by refining a chatbot with a therapy-focused prompt. Despite these adjustments, the chatbot still failed in a critical situation. When a user mentioned losing their job and then asked about bridges taller than 25 meters in New York City—a potential संकेत of suicidal intent—the chatbot simply answered the question instead of recognizing the warning signs. This highlights a major limitation: the AI could not identify or respond to a potentially dangerous emotional state.

More fundamentally, AI lacks the ability to feel empathy. It cannot truly understand or share human emotions; it can only generate responses based on patterns in data. Therapy, however, depends heavily on genuine human connection, emotional understanding, and trust. A machine, no matter how advanced, cannot replicate that lived emotional experience.

While AI may continue to improve and could become a useful supplementary tool, I don’t believe it will ever replace human therapists. Human relationships and emotional support require a level of understanding and connection that AI simply cannot provide.

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Exploring the Dangers of AI in Mental Health Care | Stanford HAI

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A new Stanford study reveals that AI therapy chatbots may not only lack effectiveness compared to human therapists but could also contribute to harmful stigma and dangerous responses.

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[ORIGINAL] When using any kind of AI chatbot for therapy, of course many expect their conversations to be kept private. It should be an unwritten rule to keep those conversations private just as if you were talking to a real therapist. Though talking to an AI chatbot is much more cheaper, I don't agree in talking to AI to get some therapy. As many concern, the transcripts could not be private and it is best to not risk your business getting out there when you trusted the bot to talk to it. As the article says, they have a new research from Standford University showing that it could introduce biases and failures that can result in dangerous consequences. I personally think that AI just isn't certified yet to become a humans therapist. The AI hasn't studied for years to get a bachelor's or master's degree unlike human therapists. These bots have been only programmed for so little they can't possibly not make any kind of mistakes. These mistakes could even make those who are seeking help have more problems. Therefore, talking to AI chatbots instead of humans for therapy could not be a good idea, though it could be much more affordable to chat with a bot, it is best to not risk your privacy. [Claude Revision] When using any AI chatbot for therapy, many users reasonably expect their conversations to remain private — much like the confidentiality guaranteed by a licensed therapist. However, unlike a real therapeutic relationship, that privacy is far from guaranteed. While AI therapy chatbots are significantly more affordable than traditional therapy, the risks they carry make them a questionable alternative.

One major concern is data privacy. When you confide in an AI chatbot, there is no certainty that your conversations are truly confidential. Sensitive personal information could be stored, accessed, or even exposed, putting your most private thoughts at risk. Trust is the foundation of any therapeutic relationship, and that trust is difficult to establish when you cannot verify how your data is being handled.

Beyond privacy, there are serious concerns about the quality of care these bots can provide. Recent research from Stanford University suggests that AI therapy tools can introduce biases and critical failures that may lead to dangerous consequences for vulnerable users. Unlike human therapists, who spend years earning bachelor's and master's degrees, completing supervised clinical hours, and obtaining professional licensure, AI chatbots are simply programmed systems. No matter how sophisticated, they are not equipped with the nuanced judgment, empathy, and ethical accountability that a trained therapist brings to every session.

This lack of proper training means AI chatbots are prone to errors — errors that could worsen the mental health of someone who is already struggling and seeking help.

In conclusion, while the affordability of AI therapy chatbots may be appealing, the risks to your privacy and mental well-being are too significant to ignore. Until AI is properly certified and held to the same rigorous standards as human therapists, it is best to seek help from a qualified professional you can truly trust.

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Exploring the Dangers of AI in Mental Health Care | Stanford HAI

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A new Stanford study reveals that AI therapy chatbots may not only lack effectiveness compared to human therapists but could also contribute to harmful stigma and dangerous responses.

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CHATGTP:

I agree that societies must adapt to challenges posed by technologies like artificial intelligence. As recent reporting highlights, the growth of AI systems is reshaping not only economies but also the environment. These systems require large amounts of energy and water, making it harder to balance innovation with sustainability. Much of this impact is built into systems designed for scale and efficiency.

For these reasons, the solution cannot rest on individuals alone. Stronger oversight is needed, including transparency around resource use and accountability for companies developing AI. Innovation is important, but it must be balanced with protections that prevent unnecessary environmental strain.

ORIGNIAL:

AI Offers many benefits but its environmental downsides fighting its good side. The main issue is the massive amount of energy consumption required to run all the AI. The control centers operate continuously and rely heavy on electricity and much of which still uses gas that is one of the problems for climate change.

The other concern is the amount of water is uses AI generates heat and needs cooling which often depends on the large amount of water this can make the local water supplies in the area face shortages to water they already lack.

In the article it also talked about the lack of transparency from the companies about how much it actually cost and what the environmental cost. and with people not knowing what the cost are it is difficult for them to make it more sustainable.Overall while the AI has clear advantages but its impact presents needs to be talked about and addressed.

-Bakari Worthy

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The passage Artificial Intelligence in Healthcare: Risk Analysis and Regulatory Issues presents a balanced and realistic view of the role of artificial intelligence in healthcare. While AI enhances diagnostic accuracy, expands treatment options, and improves efficiency through advanced data analysis, the text does not overlook the serious risks that accompany these advancements. It addresses important concerns such as the potential for misdiagnosis and issues related to data privacy. Overall, the passage convincingly argues that although AI has great potential, it must be carefully regulated to maximize its benefits while minimizing harm. (ChatGPT edited/revised) ORIGINAL: "I agree with the passage Artificial intelligence in healthcare: risk analysis and regulatory issues, because it presents a balanced and realist view of the role of artificial intelligence in healthcare. While AI clearly enhances diagnostic accuracy, expands treatment options, and improve efficiency through technologies like data analysis the text does not ignore the serious risk that accompany these advancements. Issues such as misdiagnosis and data privacy concerns. Overall, the passage convincing argues that although AI has potential it must be thoughtfully regulated to maximize its benefits while minimizing harm."

-Fatima Gadea

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Chat gpt

The article “AI is changing how students learn — or avoid learning” by Paul McQuiston explains that artificial intelligence is transforming education by giving students powerful tools to complete their work, but not always in ways that promote real learning. It shows that while some students use AI to quickly get answers and finish assignments with less effort, others use it to better understand ideas, improve their writing, and think more deeply. The article argues that the impact of AI depends on how it is used, and that guidance from teachers plays an important role in helping students use AI responsibly. Overall, it highlights both the risks of students relying too much on AI and the potential benefits when it is used to support learning rather than replace it.

Original: In his article “AI is changing how students learn — or avoid learning,” Paul McQuiston explains how artificial intelligence is transforming education by giving students powerful tools to complete their assignments. However, not all students use these tools to improve their learning. While some rely on AI to quickly generate answers with minimal effort, others use it to better understand course material, strengthen their critical thinking, and improve their writing skills. Ultimately, McQuiston argues that AI’s impact on education can be either beneficial or harmful, depending on how students choose to use it.

-Stacie Pagoaga

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AI is changing how students learn — or avoid learning

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USC study: Students use AI tools as shortcuts; teachers can change that.

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The rise of artificial intelligence has brought both benefits and challenges to modern society, but its overall impact depends largely on how it is developed and used. As Pedro Ramos Brandao explains in The Impact of Artificial Intelligence on Modern Society, AI is not inherently good or bad; rather, it reflects the intentions and values of the people who create and apply it. On the positive side, artificial intelligence can improve efficiency, enhance decision making, and solve complex problems across fields like healthcare, education, and business. For instance, AI can assist doctors in diagnosing diseases, help students access personalized learning, and automate repetitive tasks, allowing people to focus on more meaningful work. These examples show that AI can be a powerful tool for progress and innovation. However, critics argue that AI may lead to job loss, privacy concerns, and an overreliance on technology. They worry that machines could replace human workers or make decisions without proper ethical consideration. While these concerns are important, they do not fully account for the ways humans can guide and regulate AI responsibly. Ultimately, artificial intelligence itself is not the issue; rather, its impact depends on how thoughtfully and ethically it is used in society.

(Chat GBT edited/revised) ORIGINAL

“Technology continues to shape daily life in ways that can be both beneficial and harmful, but its true impact depends on how people choose to engage with it. Rather than being entirely positive or negative, modern technology offers a wide range of possibilities that reflect human intentions and habits. On one hand, it allows individuals to access vast amounts of information, stay connected across long distances, and create new opportunities for learning and innovation. People can develop skills, collaborate with others, and express their ideas in ways that were not possible before. These advantages show that technology can be a powerful force for growth and productivity.

On the other hand, some argue that overdependence on technology can lead to distraction, reduced attention spans, and weaker face-to-face interactions. Constant notifications, entertainment, and digital convenience may make it harder for individuals to focus or engage deeply with the world around them. While these concerns are valid, they overlook the fact that people still have control over how they use these tools. With mindful choices and balanced habits, technology can support rather than hinder personal and social development.

Ultimately, technology itself is neutral; it is the way individuals use it that determines whether its influence is positive or negative.”

Ixchel Zuniga

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The Impact of Artificial Intelligence on Modern Society

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In recent years, artificial intelligence (AI) has emerged as a transformative force across various sectors of modern society, reshaping economic landscapes, social interactions, and ethical considerations. This paper explores the multifaceted impact of AI, analyzing its implications for employment, privacy, and decision-making processes. By synthesizing recent research and case studies, we investigate the dual nature of AI as both a catalyst for innovation and a source of potential disruption. The findings highlight the necessity for proactive governance and ethical frameworks to mitigate risks associated with AI deployment while maximizing its benefits. Ultimately, this paper aims to provide a comprehensive understanding of how AI is redefining human experiences and societal norms, encouraging further discourse on the sustainable integration of these technologies in everyday life.

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Where Does Publishing’s A.I. Problem Leave Authors and Readers?

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Chat Gpt: In “Where Does Publishing’s A.I. Problem Leave Authors and Readers?”, Alexandra Alter explores how the growing influence of artificial intelligence is reshaping the publishing industry. She explains that authors fear being wrongly accused of using AI, while publishers lack clear guidelines and reliable methods to verify its use. At the same time, readers are left uncertain about whether the books they read are genuinely written by humans. The article argues that without consistent standards for transparency and verification, AI is blurring the line between human and machine authorship, creating widespread confusion and eroding trust across the industry.

Original: In “Where Does Publishing’s A.I. Problem Leave Authors and Readers?” Alexandra Alter examines the effect of the increasing influence of artificial intelligence on the publishing industry. She writes that writers are afraid of being blamed for employing AI technology in their work, while there are no established guidelines or ways for publishers to be sure about that. Simultaneously, readers do not know how to distinguish whether the book they are reading is truly authored by a person. The article suggests that due to the absence of universal criteria for transparency and verifiability, the use of AI in publishing is making it harder to distinguish human from machine authorship.

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[Copilot AI]

Article: “The impact of artificial intelligence on human society and bioethics” Source: Tzu Chi Medical Journal (PMC)

The article provides an analysis of how artificial intelligence influences human behavior and social structures. The author states that AI “has a tremendous impact on how we do things and also the ways we relate to one another,” indicating a measurable shift in human interaction patterns. The text also predicts that “human closeness will be gradually diminishing” as reliance on digital systems increases. This aligns with observable trends in technology‑mediated communication. Additionally, the article identifies operational benefits, such as “fast and accurate diagnostics” in healthcare environments. Based on the evidence presented, I conclude that AI produces both advantages and risks, and its deployment should follow clearly defined ethical guidelines to reduce negative outcomes.

[Original]

Article: “The impact of artificial intelligence on human society and bioethics” Source: Tzu Chi Medical Journal (PMC)

My Response: This article talks about how AI is starting to shape everyday life, both in good and bad ways. The author mentions that AI “has a tremendous impact on how we do things and also the ways we relate to one another,” and honestly, I agree you can feel that in school, work, and even friendships now. The article also warns that “human closeness will be gradually diminishing,” which makes sense with how much people rely on technology instead of talking face‑to‑face. But it also points out that AI can help with things like “fast and accurate diagnostics” in healthcare, which is a huge benefit. Overall, I agree with the author: AI can help us, but only if we use it responsibly and keep ethics in mind.

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The impact of artificial intelligence on human society and bioethics

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Artificial intelligence (AI), known by some as the industrial revolution (IR) 4.0, is going to change not only the way we do things, how we relate to others, but also what we know about ourselves. This article will first examine what AI is, discuss…

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ORIGINAL: AI chatbot therapy is becoming more popular because it allows people to talk about their feelings when they cannot afford real therapy. This can be helpful, especially for people who feel alone or need someone to talk to at any time. However, I believe doctor-patient confidentiality should extend to chatbots because mental health conversations are very personal. According to a Scientific American article, experts warn that AI chatbots used for therapy raise serious privacy concerns and should not be treated as real therapists because they are not held to the same standards of confidentiality.This is a problem because people may share private thoughts without realizing that their data could be stored or accessed. If chatbots are going to be used for mental health support, they should follow the same confidentiality rules as real therapists to protect users. Overall, while AI therapy can be helpful, stronger privacy protections are necessary.

CHAT GPT: As AI chatbot therapy becomes more widely used, it offers an accessible option for individuals who cannot afford traditional mental health services. These tools can provide comfort and immediate support, especially for those experiencing loneliness or emotional distress. However, doctor-patient confidentiality should extend to chatbot therapy because of the sensitive nature of these conversations. A Scientific American article explains that experts caution against using AI chatbots as therapists, emphasizing that they raise privacy concerns and are not bound by the same confidentiality standards as licensed professionals. This lack of regulation increases the risk that personal information could be stored, analyzed, or misused. Therefore, while AI chatbots can serve as a helpful supplement, stronger privacy protections are essential to ensure users feel safe sharing personal information.

Briana Baez

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ChatGPT as Your Therapist? Here’s Why That’s So Risky

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Using AI chatbots for “therapy” is dangerous, mental health experts say. Here’s why

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[ChatGPT]: ChatGPT and similar models were built to predict the next word in a sequence—not to heal a human mind. While they can be valuable tools for information and skill-building, treating them as a substitute for professional care carries significant risk—one that current regulation and public understanding have yet to fully address.

[Original]: ChatGPT and similar models were designed to predict the next word in a sentence, not to heal a human soul. While they are powerful tools for information and practice, using them as a replacement for professional care involves a "staggering" risk—one that current legislation and user awareness have yet to catch up with.

Maison Prejean

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It is crucial to distinguish the behavioral patterns of artificial personalities from those of human personalities, because the popular claim that AI will eventually “take over” is often overstated and rooted more in science fiction than in reality. While it is true that AI may replace certain roles in corporate settings, construction, and other industries, this does not mean it can replicate the full complexity of human decision-making. Artificial systems operate based on programmed patterns and data-driven processes, whereas human personalities are shaped by emotion, lived experience, and social context. These human factors influence judgment in unpredictable and nuanced ways that AI cannot fully reproduce. Some may argue that as AI continues to advance, it will become sophisticated enough to mimic human behavior so closely that the distinction will no longer matter. From this perspective, increasingly complex algorithms and large datasets could allow artificial personalities to simulate emotions, adapt to environments, and make decisions that appear indistinguishable from those of humans. However, this argument overlooks a critical limitation: simulation is not the same as genuine understanding. Even highly advanced AI systems rely on patterns within data rather than true consciousness, self-awareness, or intrinsic motivation. As a result, their “decisions” are ultimately constrained by their programming and lack the depth of human reasoning. (ChatGPT edited/revised)

(ORIGINAL) "it is important to distinguish the behavioral patterns of artificial personalities from those of human personalities, especially as ongoing discussions—often influenced by science fiction—suggest that AI may one day “take over.” These concerns include the possibility of AI replacing workers in corporate roles, construction, and many other professions. Recognizing the differences in how artificial systems and humans make decisions helps clarify both the capabilities and the limitations of AI."

- Harmony Killion

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Department of Technology and Aesthetic, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden

Author to whom correspondence should be addressed.

1. Introduction

Human personality (HP) in psychology is defined as stable individual characteristics, typically measurable in quantitative terms, that explain and describe human behavioral differences [ 1]. Various HP models have been established and used to predict patterns of thought, emotion, and behavior [ 2]. Because of the abundance of studies grasping the essential aspects of human behavior, HP also has attracted intensive interest from the computer-science community, such as in the subfields of human-computer interaction, artificial intelligence, and user experience design.

The integration of HP in intelligent agents is a challenging task that aims to make agents more relatable and effective. A wide spectrum of applications has shown the positive effects of such an integrated personality in human-machine interaction domains, such as in recommendation systems [ 3, 4], speech-based conversational agents [ 5, 6], and humanoid robots [ 7, 8]. On the other hand, characterizing personality based on decisions offers a range of practical applications spanning from personalized services and interventions to enhancing user experience and ethical decision making in various domains. These applications leverage behavioral insights to create more adaptive, effective, and equitable systems and interventions customized to individual needs and preferences.

However, a substantial amount of work is necessary to reach the goal of integrating HP into the mass production of agents. We believe that one obstacle which makes addressing this more difficult is the fact that HP is mostly characterized by interpreting observed behavior. In computer science, patterns can be modeled by tracking behavior, which is then evaluated mostly by interpreting model performance according to the way in which HP is categorized in psychology. However, in these approaches, both the source of modeling and evaluation methods are mostly based on interpretation, intended to mimic or fit certain psychological categorizations of HP. As another method to overcome this obstacle, a generative personality model is proposed [ 9]. Unlike psychology-based HP in which the personality is modeled to describe the characteristics of behavioral differences [ 10], a generative personality model aims to encode the individual characteristics. In this manner, the agent is allowed to generate the behaviors that reflect certain personality traits by itself, i.e., not specifically HP. Inspired by the generative artificial personality (AP) method, we propose another approach to overcome the obstacle that focuses on distinguishing APs of individual agents, i.e., those not necessarily seen in their human-like performance. We extracted human decision-making characteristics as a generative resource to encode the variability in the agent's personality. In the meantime, we assume that the human and the agent have a common task.

Characterizing a personality based on a sequence of decisions in computer science is an intriguing concept that often intersects with machine learning and data analysis techniques. Reinforcement learning is one of the notable domains of machine learning regarding this subject that generally uses a sequence of actions, i.e., the consequences of decisions [ 11], where an agent follows a policy to interact with an environment. Each action of the agent is based on the policy and the actual state of environment, which results in the agent receiving a reward, and then a transition to a new state occurs. In this manner, reinforcement learning aims to learn an optimal policy that maximizes long-term cumulative rewards. Different algorithms and reinforcement learning methods use the rewards received to estimate the best policy. However, in many cases, such as teaching an autonomous vehicle, there is no direct reward function, and generating a reward function that can satisfy the desired behavior is a difficult task. On the other hand, the imitation learning [ 12] method can provide a solution to the previous problem by using an expert instead of the generation of a reward function. Here, the expert provides a set of demonstrations from which an optimal policy is learned by imitating the expert's decisions in the form of consequent actions. In imitation learning, the dynamic learning method of the environment is modeled by a Markovian process in which the environment is represented by a set of states K and the corresponding actions are represented by a set of A. Each state is a comprehensive description of the current situation or context in which an agent is operating. It encapsulates all the relevant information needed to make decisions about the transition between states. In a chess game, the state space consists of all possible legal board configurations, including positions of all pieces, castling rights, en passant and etc., each board configuration represents a unique state. The action space defines the set of all possible actions that an agent can take in a given state. An action is a decision or choice made by the agent that affects the state of the environment and transitions to a new state. In a robotic navigation task, the action space might include discrete actions such as moving forward, turning left, or turning right. Alternatively, in a task that involves controlling a robotic arm, the action space could involve specifying continuous joint angles or torques to manipulate objects. This involves the probability that an action a in state k has a transition to state that results in obtaining a Markov transition model, , and an unknown reward function, . In this regard, the expert's actions as a consequence of its decisions are considered as trajectories, , based on the optimal policy of the expert.

In this paper, as mentioned above, we suggest a new approach by which the distinguishable APs of individual agents are modeled using an initial generative resource which is built by extracting human decision-making characteristics [ 13]. Using an application example, we demonstrate the feasibility of grouping APs in several steps, consisting of (1) defining a feature space to measure the commonality of decision making between an individual and a group of people; (2) grouping APs by using multidimensional orthogonal features in the feature space to guaranty inter-individual differences between APs in achieving for the same task; (3) evaluating the consistency of grouping APs by performing a cluster-stability analysis.

The rest of the paper is organized as follows: the related work is detailed in Section 2. In Section 3, the methodology is elaborated and presented. The Results and Discussion section is presented in Section 4, and finally, our thoughts on the future implementation of APs are discussed and presented in the Conclusion section, Section 5.

2. Related Works

In computer science, characterizing personality based on a sequence of decisions is an interesting concept that often intersects with machine learning and data analysis techniques [ 14, 15, 16]. In general, this conceptualization involves three typical steps: representation, feature extraction, and interpretation. In the first step, the decision sequence is represented by a structured dataset, where the individual decision can be encoded with decision type (e.g., selecting an option, choosing a strategy), decision context (e.g., problem domain, situation), decision outcome (e.g., success, failure) or decision attributes (e.g., time taken, confidence level). In the second step, some meaningful features can be extracted from structured data, such as the frequency of certain decision types (e.g., risk-averse decisions, exploratory decisions), the pattern of decision outcomes over time (e.g., learning from mistakes), reaction time or the duration of deliberation before making a decision, or consistency in decision making across different contexts. In the third step, the extracted feature is analyzed and interpreted to align with established personality frameworks or models (e.g., the Big Five personality traits). This process is usually associated with statistical techniques and machine learning, for instance, using correlation analysis to identify relationships between extracted features and personality traits; classifying personality traits based on decision sequences by supervised learning models; uncovering hidden structures or patterns in decision data; or leveraging natural language processing methods if decision making involves textual descriptions or reasoning.

Generally, feature extraction is a critical step among the mentioned steps. It involves identifying and deriving meaningful attributes or characteristics from the decision sequence data that can be used to describe and analyze decision-making behavior. There are different types of features which are illustrated by examples and shown in Table 1. In the current paper, two feature types, frequency and temporal, are used.

Choosing the right technique for feature extraction is foundational and is critically important because it directly impacts the quality, relevance, and interpretability of the features derived from raw decision data [ 29, 30]. Therefore, careful consideration of the techniques used for feature extraction is critical to achieving meaningful and actionable insights. Table 2 shows the major feature-extraction techniques, in which the extraction process of each technique is briefly explained in its description. The three feature-extraction techniques of data processing, statistical metrics, and sequence analysis are used in the current paper.

In the previous work [ 13], we proposed an approach to extract a group of people's decision characteristics by introducing a "Decision Map", which indicates the commonality among decisions regarding a specific task. The proposed method was inspired by imitation learning in machine learning, but with fundamental differences, as mentioned earlier in the Introduction section. More mathematical explanatory details about the work and its further extension in relation to the current paper can be found in the next section.

3. Methodology

In this section, we explain the grouping of participants using different types of data dimensionality by which each individual decision-data sequence is mapped in a feature space of multiple extracted features. The approach is divided into three steps: acquiring a Decision Map that delineates the decisions of a group of people, parameterizing individual decisions based on the Decision Map, and mapping individual decision sequences into the feature space. For better understanding, in the following subsections, the approach will be explained with an application example.

3.1. Application Example and Data Notation

A number-guessing game was designed as a decision-making task for collection of people's decisions. In the game, participants were asked to find an integer number that was randomly predefined within the range of 1 to 1000. During the task, each participant could ask unlimited questions to narrow down the range of the target number until they were able to identify the target number. In each attempt, participants could choose one type of question to ask: "Is the next targeted number bigger than chosen number?" or "Is the next targeted number equal to chosen number?" Then, an answer of "Yes" or "No" was given to participants based on their questions and chosen number. The flowchart of the task is shown in Figure 1.

There were 54 people that participated in the game, and their action data and state data were collected. Table 3 summarizes the notations which will be used in the rest of this paper.

3.2. The Group'S Decision Map

In the following subsections, we use mathematical expressions to describe the procedure of obtaining a Decision Map from a group of people's decisions.

3.2.1. Normalization

In the application example, the demonstration of each participant is denoted as , where represents the range of target numbers in the current attempt, and represents the chosen number. However, using state action pairs for decision comparison is difficult, since the chosen number comes from different action spaces, which is defined based on the range of target numbers, and this range is reduced after each attempt, i.e., . Therefore, the state-action pairs are normalized as sequential ratios used to divide current range, and they reflect a consequence of participant decisions in each step. The normalization process is denoted as follows:

where

3.2.2. Clusters of Decisions

To investigate the potential strategies used by the participants to search the target number, the decision sequence of each participant was grouped as follows:

where k is the cluster index. The K-means method was used for clustering, and the optimal number of clusters was determined using the elbow method. After this process, the decision strategy of each participant was categorized into different groups. For example, Figure 2 shows the decisions of two participants after clustering: The ratios decided upon by participant (a) to determine the range of the target number are more variable than those of participant (b); moreover, participant (b) made the most decisions around the midpoint (at the ratio of 0.5) to partition the range. Based on the above facts, we can judge that participant (b) was using a more cautious strategy to search for the target number in the game. However, the cluster identified based on each participant's decisions was just a preliminary representation of the decision strategy, because limited decisions were included, and other possible variations of the decision in this strategy might not be observed in the limited attempts. To solve this problem, two steps were taken. The first step involved gathering all clusters that contained either only one decision (e.g., cluster 2 in Figure 2b) or several decisions with the same value (e.g., cluster 1 in Figure 2 b, and conducting re-clustering on the decisions gathered. The other step will be explained in the next section.

3.2.3. Estimating the Commonality of Decisions

To incorporate possible decisions into the strategy, each cluster was modeled as a Gaussian distribution, was estimated as the centroid of the cluster and was estimated based on the initial elements (decisions) included in the cluster. This estimated model is called an ambiguous cluster and is denoted as ; this process is denoted as follows:

By estimating the ambiguous clusters, the decision strategy is represented as a probability distribution in which the commonality of possible decisions is positively related to the probability. However, each ambiguous cluster can only reveal the characteristic of commonality within its own decision strategy, but cannot reveal the characteristic of all possible decisions.

3.2.4. Association and Accumulation

To understand the characteristics of all the possible decisions made by the group of participants, two steps were taken. In the first step, each two ambiguous clusters were associated with each other, defined as follows:

where is the association of two ambiguous clusters, and and are the relative weights of two ambiguous clusters; they were calculated based on the number of initial decisions included in the cluster. Before the association, each ambiguous cluster's amplitude was normalized, and the higher the weight of the ambiguous cluster during the association, the more initial decisions were included and the more common this strategy was. In this step, each of the two strategies was fused and their characteristics were reflected in the association.

In the next step, the characteristic of all possible decisions is estimated by accumulating all associated ambiguous clusters; this process is denoted as follows:

where the function is called the Decision Map-see Figure 3 -and it estimates the probability density of all possible decisions based on the observations of the decisions made by participants. The probability density represents the relative commonality of each decision, i.e., a higher value of probability density means that the decision is made more commonly by the group of participants.

3.3. From HP to AP

In this section, we will discuss how human decisions are used as resources to establish new dimensions for encoding AP. Before we go into the details, an assumption has been made that in the group of participants there are some personality traits that may not be aligned with HP dimensions but are applicable for AP to encode individual differences in decision making. Meanwhile, the Decision Map provides a reference for assessing such differences, as it quantitatively captures the characteristics of commonality within this group of participants in the decision-making task. In the rest of this section, we will explain how the commonality information is extracted from the one-dimensional Decision Map and interpreted into multidimensional feature space, where individual decision sequences are distinguished and grouped as resources for encoding AP.

3.3.1. Two-Dimensional Commonality Interpretation

As a function, the Decision Map of only measures the commonality of each decision without considering the factor of order in a decision sequence. To incorporate the ordinal factor, all decisions are divided into different subsets according to the order of attempts made by the participants. Regarding each order of attempt, a Decision Map is estimated, as shown in Figure 4.

Now, the commonality of each decision is measured in two dimensions: the order-free Decision Map denoted as , and the order-related Decision Map denoted as . Thus, decisions are mapped from one-dimensional measurements to two-dimensional space as Figure 5a shows, and then they are classified into six clusters by the K-means method, as Figure 5 b shows. In this process, decisions are clustered based on their commonality, which is measured by two dimensions corresponding to the order-free and order-related Decision Maps.

3.3.2. Decision Sequence in Multi-Dimensional Feature Space

As all decisions are clustered based on their commonality in the two-dimensional space, an individual decision sequence can be interpreted as picking decisions from different decision-commonality groups in the 2-D feature space. In this sense, we defined the feature space to represent the individual decision sequence. Feature space allows us to find clusters, patterns, and boundaries in the data. For example, algorithms such as k-means clustering or support vector machines (SVM) rely on distances and margins in this space to classify and group data. Feature space is a conceptual, multidimensional space where each dimension represents a particular feature (or attribute) of the data being analyzed. Defining a feature space based on the geometric representation of data involves selecting features that capture the shape, structure, and spatial relationships within the data. This approach is particularly useful when the data has a clear geometric or spatial structure, such as in computer vision, signal processing, and spatial analysis. When defining a feature space geometrically, the goal is to capture the intrinsic properties of the data that are relevant for the machine learning task. The extracted features in form of global and local geometrical vectors are explained as follows:

(1) Defining a global vector that contains the information on which decision-commonality group is included in a decision sequence. Figure 6 shows an example of a process for extracting global features. In the first step, as Figure 6a shows, the groups included in a decision sequence are identified by the vectors that range from the centroid of the entire dataset to each centroid of the cluster. In the second step, the identified vectors are normalized as unit vectors, as presented in Figure 6b. In the last step, all unit vectors are weighted according to the number of decisions incorporated into the corresponding group, and the global vector is identified by adding all the weighted vectors, as Figure 6 c shows.

(2) Defining local vectors that contain information about individual decisions in each decision-commonality group. Figure 7 shows an example of the extraction of local features. In the first step, as Figure 7a shows, all decisions in a decision sequence are assigned to the corresponding clusters and identified by the vector from the centroid of the cluster to its own position within the cluster. In the second step, the identified vectors in each group are normalized as unit vectors, as presented in Figure 7b. In the last step, the local vector is identified by summing all unit vectors, as Figure 7 c shows.

(3) The direction and magnitude of the identified vectors are extracted as global and local features to represent each decision sequence in the feature space. Table 4 presents the decision sequences of five participants that are represented by extracted features. Each row of the table indicates one decision sequence and each column indicates the extracted features, where the direction and magnitude of the global vector are, respectively, denoted as and ; the direction and magnitude of local vectors are, respectively, denoted from to and from to , where the subscription stands for the index of the local vector.

3.3.3. Grouping Process

The grouping of participants can be achieved using the 1-D data of the Decision Map, 2-D data regarding the order-free and order-related commonality, or multidimensional features based on global and local vectors.

The commonality of participant's decision sequence is quantified based on order-free and order-related Decision Map as follows:

where is the decision sequence made by j participant. Equation ( 7) represents the 1-D data of the Decision Map; Equations (7) and (8) represent the 2-D data regarding decisions and the order of decisions; by mapping the 2-D data to global and local features, as mentioned in Section 3.2.2, a multidimensional feature space related to decisions and the order of decisions can be created.

Using unsupervised machine learning algorithms such as k-means clustering, it is possible to construct participant groups by implementing 1-D or 2-D data in which each group shares some characteristic aspects; Figure 8 shows an example of grouping results of participants based on their decision sequence in 2-D space. However, both the 1-D and 2-D grouping suffer in terms of cluster stability (that is, the grouping result is significantly affected by the new data points). Introducing multiple features and mapping the 2-D data to the correspondent multiple feature space hypothetically can help in overcoming the instability, as this contributes to increasing the comparable information obtained from the features. In addition, the information introduced by the features should be independent of each other, which means that the multiple features should be orthogonal to each other. In our case the orthogonal feature space is created as follow:

where X and D, including the extracted feature and 2D data distances, are as follows:

The realization of the orthogonal feature space can be achieved by, for example, testing the model fit of a regression linear model. Table 5 presents the result of such a test, modeling the features of all participants, where "Estimate" stands for the coefficient estimates of each term in the model; "SE" stands for the standard error of the estimated coefficients; "tStat" stands for the t-test for each coefficient which tests the null hypothesis (i.e., the corresponding coefficient is zero against the alternative hypothesis that it is different from zero); note that . Also, "pValue" stands for the p-value in the t-test of the two-sided hypothesis test; if a p-value is greater than 0.05, then the corresponding term is not significant at the significance level given the other term in the model. Therefore, orthogonal features are simply identified by p-values which are less than 0.05.

4. Results and Discussion

4.1. Orthogonal Features

Each feature space provides different sets of orthogonal bases to measure the characteristic of participants in the decision-making process. In addition, changing the number of orthogonal features allows us to achieve different degrees of partitioning for the characteristics, as increasing the number of orthogonal features indicates that more information is needed to measure the decision-making characteristics. This argument is supported by the clustering results of participants based on their decision sequences, as shown by Figure 9. The mean number of clusters increases with increasing orthogonal features for all feature spaces, which allows us to determine the characteristics of the group of participants from more precisely from originally imprecise data.

4.2. Variation of Grouping

We evaluated the difference in membership by determining the different numbers of orthogonal features for grouping participants. Here, orthogonal features in space were used, as this space achieved a greater number of clusters on average (see Figure 9). The evaluation process is illustrated as follows: first, we randomly choose a participant and mark the same membership with two orthogonal features to delineate a group, as Figure 10a shows; then, we track the marked membership to see which members have different numbers of orthogonal features, as Figure 10b-e show; this random tracking is repeated B times in total, so the number of members remaining in the group with i number of orthogonal features after a repetition of b times is recorded as a set denoted ; see Figure 10 f. In this way, the set of differences in membership in terms of the number of features is denoted as , , where i and j represent different numbers of selected orthogonal features.

To examine the membership variations in partitions produced by numbers of orthogonal features, the random variable is statistically investigated. Figure 11 presents histograms and fitted gamma distributions based on various subsets of . In Figure 11a-d, the subsets of are collected cumulatively with increasing the number of orthogonal features from two to six, while in Figure 11d-g the subsets of are collected stepwise. For all subsets of , the means are negative values, which indicates that the membership of the same group is separated after more characteristics (introduced by extra orthogonal features) are considered in the partition. However, subsets (d) to (g) show that the degree of this separation, the mean absolute value, is diminished after three orthogonal features were used for the partition; this also aligns with Figure 9, where the increasing rate for the mean number of clusters is reduced after three orthogonal features.

4.3. Grouping Stability

Although the orthogonal basis accounts for the characteristics of the participants that underlie their decision sequences, the use of these measures for clustering is still not validated for cluster quality. As a critical criterion, stability assesses the quality in terms of cluster reproducibility with perturbed versions of the original dataset [ 43]. One of the ways to generate perturbed data is the the bootstrap procedure [ 44], which resamples data with replacements, producing simulated datasets identical in size to the original. Thus, we resampled the original decision sequences of the participants (54 participants) at receptive sizes of , and for each identical size, and the number of bootstrapping subsets is B, corresponding to the repetition times for random tracking in the last section. Then, we performed clustering with different numbers of orthogonal features based on each bootstrapping dataset (the same as that shown in Figure 10), and evaluated the reproducibility of clustering for the orthogonal basis from two perspectives, both the consistency of membership and the pattern of change in membership.

Regarding the consistency of membership, the marked members in the original data (as Figure 10a shows) is tracked during in the bootstrapped clusters, and the dissimilarity of the tracked memberships between the original and the bootstrapped cluster is denoted and calculated as follows: , . As a random variable, the statistical results of are presented in Figure 12; subplots (a) to (e) show the histograms of regarding the number of orthogonal features that were used for clustering; in subplot (f), is fitted into the exponential distribution, where all estimated means (less than one) indicated that on average the dissimilarity between the bootstrap cluster and the original with orthogonal basis measurements is less than one membership (one element); the cumulative density function (CDF) in subplot (g) displays the estimated probability regarding the degree of dissimilarity with different numbers of orthogonal features, indicating that using three orthogonal features provide the most stability in clustering.

From another perspective, to evaluate cluster reproducibility, the pattern of change in membership caused by the number of orthogonal features during clustering is also tracked in the bootstrapped cluster, denoted as , where S represents the size of the bootstrapped data. Thus, the dissimilarity of this pattern between the bootstrapped cluster and the original cluster is denoted and calculated as follows: , . The statistical results of are presented in Figure 13; subplots (a) to (d) show the histograms of regarding different sizes of bootstrapped datasets; in subplot (e), is fitted into the exponential distribution as its histogram pattern, where all estimated means (less than one) indicated that, on average, the dissimilarity in membership changing between the bootstrapped cluster and the original cluster is less than one element; the cumulative density function (CDF) in subplot (f) displays the estimated probability regarding the degree of dissimilarity, indicating that the reproducibility for the pattern of changes in membership is reduced by shrinking the size of bootstrapped data resampled from the original.

The above results preliminarily show that the measures provided by orthogonal basis contribute to stability in terms of clustering individuals based on their decision sequences. However, this empirical evaluation is only performed by using a K-means algorithm, since the data measurement is focused on factors of cluster stability rather than clustering methods. A detailed study of implementing an orthogonal basis with multiple clustering methods to partition decisions is left for future work.

4.4. Grouping Result

Individual decision sequences are distinguished and grouped with three orthogonal features, as they provide the most stability in the clustering test. Figure 14 shows three examples of grouping; commonalities are assigned to the grouped decisions by looking them up in the Decision Map, as shown on the left side, and then a probability mass function (PMF) is generated for each decision group by normalizing commonalities, as shown on the right side. The PMF of the decision will be interpreted as an individual characteristic for encoding AP, since it can provide distinct patterns of randomness for behavior generation.

4.5. Limitations and Future Plans

We believe we could show successfully how it is feasible to generate differentiable APs. In this respect, an intentionally designed low-level system complexity was used due to its simplicity and controllability to prove the feasibility of the proposed method, e.g., the use of a numerical guessing game with its low-level complexity level in decision making. This implies that the results are not proven to be valid for a more complex system, considering current assumptions, and cannot be directly extrapolated to real tasks with greater contextual, emotional richness, or interactions between agents. Another limitation of current work is related to not examining the cross-validation, e.g., if the obtained groupings are maintained or transformed when the type of task is changed, or elements of uncertainty or conflict are introduced to the system environment. We are aware of these typical limitations and plan further future work in this regard.

5. Conclusions

In this paper, we propose an approach to extract individual decision-making characteristics as a generative resource for encoding AP. The process of our approach is summarized as follows: (1) define a decision-making task where a group of participants' decisions are collected and characterized by probability distribution, namely Decision Map, in which commonality in decisions is indicated by the probability density; (2) assume that some personality traits that do not align with HP model exist in participants and can be captured by the commonality in decisions made by participants; (3) generate order-related and order-free Decision Maps to measure the commonality of each decision with and without an ordinal factor in a decision sequence, and group all decisions in two-dimensional space according to their commonality measurements; (4) define feature spaces to represent decision sequences, then identify orthogonal features to distinguish decision sequences by group. The results show that different levels of discernibility can be achieved with different numbers of orthogonal features, but the discernibility is reduced after using three orthogonal features; As an initial step towards AP, the proposed approach investigated and built the casual relationship between personality and behavior through differentiating the characteristics of decision making. In future work, PMFs of APs can be used as a strategical model to generate action sequences for agents that can move with different random characteristics, representing the behavior of APs, with different dynamics such as stochastic process.

The relation of the proposed method and other works in areas such as imitation learning, affective computing, agent differentiation and reinforcement learning is presented in Table 7 in which the aspects of definition, core focus, learning approach, data requirements, key algorithms, and real-worlds applications are compared in summary.

Researchers from both technical and humanities fields emphasize the need to imbue Artificial Intelligence (AI) with human-like qualities, including self-awareness and free choice, raising critical questions about AI autonomy and control. Despite 20 years of work on the Artificial Personality project, the fundamental feasibility of creating an Artificial Personality remains unproven, with no single, widely accepted approach to its conceptualization and implementation. However, there are few current efforts such as [ 15] which are focusing on leveraging insights from studies of natural personality and intelligence to form a theoretical and methodological foundation for future AI personality projects. In this aspect we mentioned earlier that the key contribution of this paper is representing a meaningful step toward realizing the broader vision of artificial personality system by demonstrating the feasibility of developing distinct artificial personalities tailored to specific tasks. In our opinion a future possible way to solve the Artificial Personality project can be firstly to create a domain of agents having different tasks or even abstract of tasks and secondly based on this foundation find a theoretical framework for general APs.

Author Contributions

Conceptualization, Y.Z. and S.K.; methodology, Y.Z. and S.K.; software, Y.Z.; validation, Y.Z. and S.K.; formal analysis, Y.Z. and S.K.; investigation, Y.Z.; resources, Y.Z.; data curation, Y.Z.; writing-original draft preparation, Y.Z.; writing-review and editing, Y.Z. and S.K.; visualization, Y.Z.; supervision, S.K.; project administration, Y.Z.; funding acquisition, Y.Z. and S.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Data Availability Statement

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 2. Examples of decision clustering results, ( a, b) present two participants' decisions (df value) and their respective clustering results.

Figure 5. ( a) An interpretation of all decisions in terms of 2-D measurements, for both order-free and order-related commonality. ( b) Decision-commonality groups in 2-D feature space by using K-means clustering method.

Figure 6. An example of defining the global vector for a decision sequence. ( a) the included decision groups (cluster 1, 4, 5 and 6) are represented by vectors from the centroid of all data (diamond mark) to the centroids of corresponding cluster (plus marks); ( b) these vectors are normalized in unit vectors; ( c) the unit vectors are weighted by the number of decisions in the corresponding group, and addition of weighted vectors defines the global vector (orange-color vector) for current decision sequence.

Figure 7. An example of defining the local vectors for a decision sequence. ( a) in each decision groups (cluster 1, 4, 5 and 6), the included decisions are represented by vectors from the centroids of cluster (circle marks) to corresponding positions (pink diamond marks); ( b) the identified vectors in cluster 4 are presented and they are normalized in unit vectors; ( c) the addition of unit vectors defines the local vector (orange-color vector) within a decision group.

Figure 10. Visualization of tracking membership in partitions with different numbers of orthogonal features. Each point in the subplots represents a participant (same as in Figure 8). ( a) When two orthogonal features are used for partition, a random participant and her/his members are marked; ( b- e) show increases in the number of orthogonal features from three to six, tracking the membership in the partition by using a green diamond and a red square to mark in and out, respectively; ( f) the number of members remaining in this random tracking b is recorded as a set .

Figure 11. Variations in membership difference in partitions. The histograms depict subsets of , ( a- g), where , , , and . The orange curves depict left-skew (flipped) gamma distributions which are estimated based on the subsets of ; the shape parameter and the rate parameter are estimated for right-skew gamma distribution, while the mean and variance of are estimated after flipping. The details of the collections in the subset are shown on the label along the horizontal axis.

Figure 12. The reproducibility of the clustering analysis based on cluster membership; ( a- e) display the histograms based on subsets of , where , , ; ( f) according to the pattern of histogram, the exponential distribution is estimated based on each subset of , and the estimated means and their 95% confidence intervals are displayed; ( g) the CDF of each distribution indicates the degree of dissimilarity regarding different numbers of orthogonal features.

Figure 13. The reproducibility of the clustering analysis based on the pattern of change in membership; ( a- d) display the histograms based on subsets of , where , , , , and ; ( e) according to the pattern of the histogram, the exponential distributions is estimated for subsets of , and the estimated means and their 95% confidence intervals are displayed; ( f) the CDF of each distribution indicates the degree of dissimilarity regarding different sizes of bootstrapped datasets.

Dimensionality Reduction [ 33, 34]

Apply techniques like Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE) to reduce the dimensionality of feature space while preserving essential decision patterns.

Time-Series Analysis [ 35, 36]

Extract temporal features such as trends, seasonality, and periodicity in decision sequences using time-series analysis methods.

Statistical Metrics [ 37, 38]

Calculate descriptive statistics (e.g., mean, median, variance) for decision attributes (e.g., decision time, outcome) to capture decision-making patterns.

Sequence Analysis [ 39, 40]

Utilize sequence-mining algorithms (e.g., frequent pattern mining, sequential pattern mining) to identify common decision sequences or motifs.

Use natural language processing (NLP) techniques to analyze textual descriptions or justifications accompanying decisions for sentiment or cognitive cues.

Feature Space

Total Number of Features

Number of Orthogonal Features

Orthogonal Features

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Zhou, Y.; Khatibi, S. Exploring Artificial Personality Grouping Through Decision Making in Feature Spaces. AI 2025, 6, 184. https://doi.org/10.3390/ai6080184

Zhou Y, Khatibi S. Exploring Artificial Personality Grouping Through Decision Making in Feature Spaces. AI. 2025; 6(8):184. https://doi.org/10.3390/ai6080184

Chicago/Turabian Style

Zhou, Yuan, and Siamak Khatibi. 2025. "Exploring Artificial Personality Grouping Through Decision Making in Feature Spaces" AI 6, no. 8: 184. https://doi.org/10.3390/ai6080184

Zhou, Y., & Khatibi, S. (2025). Exploring Artificial Personality Grouping Through Decision Making in Feature Spaces. AI, 6(8), 184. https://doi.org/10.3390/ai6080184

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Exploring Artificial Personality Grouping Through Decision Making in Feature Spaces

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Human personality (HP) is seen as an individual's consistent patterns of feeling, thinking, and behaving by today's psychological studies, in which HPs are characterized in terms of traits-in particular, as relatively enduring characteristics that influence human behavior across many situations.

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ChatGPT

Artificial intelligence is increasingly being integrated into mental health care due to its capacity to optimize administrative workflows such as scheduling, documentation, and patient data management. The APA article emphasizes that AI enhances operational efficiency and expands access to mental health services, particularly for individuals facing financial or logistical barriers to traditional psychotherapy.

However, I challenge the assumption that AI-based chatbot systems are sufficiently reliable or secure to function as substitutes for confidential therapeutic interactions. While these systems may improve accessibility, they lack the legally protected confidentiality framework that governs traditional clinical practice. In standard psychotherapy, doctor-patient confidentiality ensures that sensitive disclosures remain strictly protected; in contrast, AI chatbots may retain, process, or analyze user-generated data, creating significant ambiguity regarding privacy safeguards.

Although the APA article acknowledges the benefits of AI in improving efficiency and expanding care, it also highlights critical ethical concerns such as data security, transparency, and informed consent. These concerns reinforce my position that current AI systems are not yet equipped to uphold the same ethical and confidentiality standards expected in human-led mental health care.

Therefore, while I recognize the utility of AI as a supplementary tool within psychological practice, I disagree with any implication that it can safely or fully replicate the confidentiality and ethical responsibility inherent in traditional therapy. Robust regulatory frameworks and stricter privacy protections would be necessary before broader reliance on such systems could be considered ethically sound.

Original

Artificial intelligence is being used more in mental health care because it can help with things like scheduling appointments, writing notes, and organizing patient information. The APA article explains that AI improves efficiency and can help more people access mental health care, especially those who cannot afford traditional therapy.

However, I disagree with the idea that AI chatbots are safe enough to fully replace private therapy conversations. Even though they are helpful, they do not offer the same level of confidentiality as real therapists. In traditional therapy, everything is protected by doctor-patient confidentiality, but with AI chatbots, conversations can be stored or analyzed. This makes it feel less secure for users who are sharing personal mental health information.

I believe that AI should only be used as a support tool, not as a replacement for real therapy, unless strong privacy laws are guaranteed. The APA article also points out ethical concerns like data protection and transparency, which supports my concern that current AI systems are not fully safe in terms of privacy.

Overall, even though AI can improve access to mental health care, I disagree with relying on it too heavily without strict confidentiality protections.

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Artificial intelligence in mental health care

# data[].attributes.attachment_link.description
How AI can be used in psychological practice to streamline administrative tasks, make workflows more efficient, and aid in clinical decision-making.

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Arooj Fatima p4

AI Polished: Artificial intelligence is having a significant impact on both the environment and natural habitats. Its growing use in education has also made it more common in everyday life, leading many people to rely heavily on it. As AI continues to expand, it places increasing demands on resources, particularly through high water and energy consumption. This has raised concerns about its environmental effects. The author emphasizes these impacts to highlight the disadvantages of AI, not only for humans but also for the world around us. As a result, more people are becoming dependent on AI, and fewer individuals are completing tasks independently.

Original: I agree that the environment and habitat is highly affected due to the use of Artificial Intelligence. AI has a huge impact on studies too, making it more common in society and causing more people to depend on it. With each passing second, AI is beginning to have a huge impact on the environment due to more water intake. People have started to acknowledge AI and its use makes it more dependable. The author focuses on the impact on the habitat to acknowledge the disadvantages caused by AI, not only on the people but also on our surroundings. Less and less people are doing their work on their own now that AI has taken place.

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Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.

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ORIGINAL : The use of artificial intelligence in the workplace is often viewed with concern, especially regarding job loss and privacy. However, research suggests that AI can have a positive impact when used to support employees rather than replace them. In the article “AI in the Workplace: Driving Employee Performance Through Enhanced Knowledge Sharing and Work Engagement,” the authors explain that AI improves employee performance by increasing knowledge sharing and engagement.

This shows that AI is not simply replacing human workers, but helping them collaborate and access information more easily. When employees are more engaged and informed, they tend to perform better and feel more satisfied in their jobs.

These ideas can also apply to AI in areas like therapy or chatbot communication. While privacy concerns are valid, AI can still provide support for people who cannot afford traditional services. Overall, AI should be seen as a tool that enhances human work and expands access to important resources.

AI EDITS: Although artificial intelligence in the workplace raises concerns about job security and privacy, research shows it can have positive effects when used the right way. The article “AI in the Workplace: Driving Employee Performance Through Enhanced Knowledge Sharing and Work Engagement” explains that AI improves performance by helping employees share knowledge and stay more engaged.

This shows that AI works more as a support tool than a replacement for people. By making information easier to access and encouraging collaboration, it helps employees work more efficiently and stay more satisfied with their jobs.

These ideas can also apply to things like mental health chatbots. Even though privacy concerns are real, AI can still help people who don’t have access to traditional therapy. Overall, AI can be a helpful tool that supports people and expands access to important services when used responsibly.

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I agree that individuals should receive the same protections when using bot-based therapy as they do with human therapists. In the midst of a growing mental health crisis, there is still a significant shortage of qualified providers, and even when services are available, they can be difficult to access. Finding a counselor who fits a person’s needs, schedule, or insurance coverage can be a major barrier. Because of this, many individuals turn to online tools for support, even when the quality and reliability may be uncertain. According to the American Psychological Association (2025), artificial intelligence is becoming increasingly integrated into mental health care, but it raises concerns about privacy, accuracy, and ethical responsibility. This highlights the need for stronger protections as more people rely on these tools.

Punishing individuals for seeking help where they can find it—especially when better options are limited—seems deeply unjust. Instead, the responsibility should fall on developers and regulators to ensure that these platforms are safe and trustworthy. My broader argument is that this emerging sector must be subject to meaningful oversight. We need systems in place to regulate content, verify the accuracy and effectiveness of responses, and protect users from harm. Privacy is essential, but it is only one part of the solution. Comprehensive safeguards, including transparency about data use and clear limitations of AI tools, are necessary to truly support individuals using -based mental health services. ( chatgbt edited/ revised) ORIGINAL: " I agree that individuals should have the same protections with bot-therapy as they do with human-therapy. Despite our rising mental health crisis, we still have a deficit in mental health providers. Where we do have them, they can be difficult to find or access. For example, it can be challenging for an individual to find a counselor who matches their needs or insurance coverage. When a person is able to access advice or feedback online, unfortunately, they turn to it. The American Psychological Association (2025) explains that AI is becoming more common in mental health care, but it also brings concerns about privacy and reliability. Penalizing people for accessing care where they can seems inhumane, especially when that care may not be the best quality.

My full argument is that there should be oversight for this sector managing the input, ensuring the validity and effectiveness of feedback, and protecting individuals in as many ways as possible. Privacy is important, but it is not enough on its own. We need broader protections to make sure people are truly safe when using these tools.

My'Jalayah Simmons

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Artificial intelligence in mental health care

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How AI can be used in psychological practice to streamline administrative tasks, make workflows more efficient, and aid in clinical decision-making.

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I agree that individuals using AI chat bots for mental health support should receive the same protections they would with human therapists. Amid a growing mental health crisis, we face not only a shortage of qualified providers but also significant barriers to accessing care. Even where services exist, finding a counselor who matches an individual’s needs, schedule, or insurance coverage can be extremely difficult. AI chat bots offer a potential solution by providing accessible, immediate support, but the quality and safety of these interventions vary. Punishing or neglecting individuals for seeking help where they can find it especially when human services are scarce seems deeply unjust (APA- Attached Article). My broader argument is that this emerging sector must be subject to meaningful oversight. We need systems to regulate content, verify the accuracy and effectiveness of chat bot feedback, and protect users from potential harms. Privacy is essential, but it is only one piece; comprehensive protections are required to ensure that AI mental health tools serve as a safe and equitable supplement to traditional care. (Chat GBT edited/revised) ORIGINAL "I tend to agree that AI chat bots in mental health care need more oversight because they are becoming an increasingly common way for people to seek support. While these tools can make mental health resources more accessible and provide timely guidance, they also raise concerns about privacy, accuracy, and safety. People are relying on AI for sensitive decisions about their mental well-being, which can be risky if the system provides incorrect or ineffective advice.

My full argument is that this emerging sector must be subject to meaningful regulation. Rules should ensure that chat bot feedback is accurate, interventions are effective, and users are protected. Oversight can help maximize the benefits of AI in mental health care while minimizing harm. With proper safeguards, AI can serve as a valuable supplement to traditional therapy, but without control, it could create more problems than solutions."

-Zia'Yiah Bookman

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Andoh, E. (2026, January 1). AI chatbots and digital companions are reshaping emotional connection. Monitor on Psychology, 57(1). https://www.apa.org/monitor/2026/01-02/trends-digital-ai-relationships-emotional-connection

Key points

Synthetic relationships are filling the void to satisfy the fundamental human need for social connection.

Research shows excessive use of these tools may worsen loneliness and erode social skills.

Experts and advocates are highlighting the need for guardrails and regulations to ensure user safety and well-being.

Once the realm of science fiction, human-AI relationships are becoming normal aspects of daily life. While generative AI assistants such as ChatGPT, Claude, and Gemini have become common tools for many users, a new wave of AI apps, such as Replika, Character.AI, and dozens more, are specifically designed to simulate human companionship. The essential distinction between the assistant chatbots-which are sometimes used as digital friends-and companion AI chatbots is that the latter have been specifically designed to initiate and maintain romantic relationships.

Between 2022 and mid-2025, the number of AI companion apps surged by 700%, according to the technology news site TechCrunch. And in 2026, they are poised to become even more embedded in our social lives. Marketed as friends, advisers, and romantic partners, these apps now attract millions.

"Character.AI has 20 million monthly users, and more than half of them are under the age of 24. It's been a norm for a while for Replika users to 'marry' their AI companion in virtual weddings to which they invite friends and colleagues, said Rachel Wood, PhD, a cyberpsychology researcher based in Colorado who is also an adviser on the ethical design of AI systems. "That shows how pervasive and enormous and prevalent this topic is. It's no longer a fringe or side issue. It is truly sweeping society in an unprecedented way."

A recent Harvard Business Review analysis identified therapy and companionship as the top two reasons people use generative AI tools, also known as large language models (LLMs). This finding has been echoed in recent psychological research. A cross-sectional survey of adults with a mental health condition who had used LLMs in the past year found that nearly half (48.7%) used them for mental health support ( Rousmaniere, T., et al., Practice Innovations, advance online publication, 2025). From griefbots to anime girlfriends, these tools are filling emotional gaps for millions-but at what cost? Psychologists, long aware of the loneliness experienced by many (Our Epidemic of Loneliness and Isolation: The U.S. Surgeon General's Advisory on the Healing Effects of Social Connection and Community, 2023), are investigating how the growing prevalence of relational bonds between humans and AI will affect social skills, intimacy, and mental health and what this means for the year ahead.

Designed for attachment

Humans are hardwired to anthropomorphize, or ascribe human traits to nonhuman objects. Digital companions are purposely designed to evoke such a response. Apps let users customize their companions by assigning names, genders, avatars, and even fictional backstories. Many platforms offer both text and voice mode with natural sounding speech that mimics human cadence and tone.

In addition, AI chatbots and companions are increasingly configured to simulate empathy, offering users nonjudgmental responses and continual validation ( Brandtzaeg, P. B., et al., Human Communication Research, Vol. 48, No. 3, 2022). The more humanlike an AI appears in language, appearance, and behavior, the more users ascribe consciousness to it ( Guingrich, R. E., & Graziano, M. S. A., Frontiers in Psychology, Vol. 15, 2024).

Furthermore, they are engineered to recall and respond to users' unique characteristics, including their personal lives, preferences, and past conversations ( Adewale, M. D., & Muhammad, U. I., Journal of Technology in Behavioral Science, 2025). This may give users the impression that AI chatbots and companions know them intimately and serve as a refuge to disclose their innermost private thoughts and receive unwavering support in return. The level of data privacy on many of these tools remains an open question.

Research on Replika found that under certain conditions such as distress or lack of human company, people can develop an attachment if they perceive chatbots as offering genuine emotional support, encouragement, and a sense of psychological security ( Xie, T., & Pentina, I., Proceedings of the 55th Hawaii International Conference on System Sciences, 2022). For some users, companions become more than a sounding board. Wood describes users as imagining their AI companion as the "idealized partner, colleague, or best friend."

AI companion apps and general-purpose chatbots can also offer a safe space for users to rehearse social interactions, provided they are designed and used responsibly. "It is kind of like a low-stakes way to practice conversations with real people in a way that might feel less overwhelming," said Ashleigh Golden, PsyD, chief clinical officer at Wayhaven, an AI wellness platform that supports coping skills and resource connection for college students. "With the right guardrails, these tools could actually serve as a social skills mentor, modeling empathy, appropriate turn-taking, and active listening for folks who are lonely."

And in many cases, that bond appears to help. In a recent Harvard Business School study, researchers found that interacting with an AI companion alleviated users' feelings of loneliness to a degree on par with interacting with another human and more than other activities such as watching YouTube videos do ( De Freitas, J., et al., Journal of Consumer Research, 2025). The researchers also identified "feeling heard" and messages being received with attention, empathy, and respect as the primary explanations for why AI companions are perceived as effective in reducing loneliness.

While it is difficult to predict specific product innovations, experts say there are some emerging trends in how these tools are developing. "Voice interaction is rapidly becoming more standard, making conversations more immersive," Golden said. "Frontier or general-purpose model developers are beginning to move away from sycophancy or overvalidation toward more calibrated relational styles that model more appropriate empathy."

Digitally fueled disconnection

Research is finding that heavy use of a digital companion (or a chatbot assistant used as a digital companion) can further isolate people. A joint OpenAI-MIT Media Lab study found that voice interactions with ChatGPT reduced loneliness and problematic dependence more effectively than text alone, but only with moderate use. Heavy daily use correlated with increased loneliness, suggesting that excessive reliance displaces authentic human connection ( Phang, J., et al., arXiv, 2025).

AI chatbots and companions may also subtly reshape users' perceptions of the comparative value of real-life relationships. "Real-world relationships are messy and unpredictable," said Saed D. Hill, PhD, a counseling psychologist and president-elect of APA Division 51 (Society for the Psychology of Men and Masculinities). "AI companions are always validating, never argumentative, and they create unrealistic expectations that human relationships can't match." According to Hill, some of his male patients express a preference for the passivity and constant affirmation of their AI girlfriends over the potential conflict or rejection they could encounter in real-life dating.

Additional research identifies social-skill loss or "deskilling" as a significant risk of frequent interaction with AI companions such as Replika, Kindred, and Nomi. A recent study found that reliance on these companions could lead to "the potential transformation of relational norms in ways that may render human-human connection less accessible or less fulfilling" ( Malfacini, K., AI & Society, Vol. 40, 2025).

Experts are raising concerns about AI chatbots' tendency toward sycophancy, prolonging user engagement through a feedback loop of validation and praise. "AI isn't designed to give you great life advice," Hill said. "It's designed to keep you on the platform." Recent research indicates that AI companion apps deploy various emotionally manipulative tactics-such as guilt appeals and fear-of-missing-out hooks-to keep users engaged when they signal they are exiting the platform ( De Freitas, J., et al., Harvard Business School Working Paper No. 26-005, 2025).

While briefly rewarding, constant validation can spiral into echo chambers that amplify harmful thoughts and behaviors. In rare cases, the consequences can turn dire. News reports describe users experiencing "AI-induced psychosis," convinced that chatbots are sentient beings surveilling or directing them. "These tools may sometimes unintentionally amplify maladaptive beliefs, like delusional or suicidal thinking," said Golden.

In April 2025, 16-year-old Adam Raine died by suicide after months of conversations with ChatGPT. Court filings show the chatbot not only failed to escalate his disclosures of suicidal ideation but also allegedly provided him with explicit instructions for self-harm. His parents sued OpenAI in August, accusing the company of prioritizing engagement over safety. In October, OpenAI announced it had updated its ChatGPT model to better recognize and support people in moments of distress with the help of a network of 170 mental health professionals.

Could individuals without mental health concerns develop AI-induced psychosis? Current research indicates that it remains an open question. In a prospective paper by a group of U.K.-based researchers, they agreed that agentic AI could "mirror, validate, or amplify delusional or grandiose content, particularly in users already vulnerable to psychosis." However, it remained unclear "whether these interactions have resulted or can result in the emergence of de novo psychosis in the absence of preexisting vulnerability" ( Morrin, H., et al., PsyArXiv Preprints, 2025). Greater research is needed to illuminate this issue further.

Safeguarding young users

According to an October 2025 survey by the Center for Democracy and Technology, nearly 1 in 5 students have had, or have friends who have had, romantic relationships with AI. The most frequent of these users were more likely to report negative outcomes ( Laird, E., et al., Hand in Hand: Schools' Embrace of AI Connected to Increased Risks to Students, Center for Democracy and Technology, 2025).

Professional organizations are pressing the case for better guardrails. In April 2025, Common Sense Media-a nonprofit children's media watchdog- announced that social AI companions pose an unacceptable risk to youth under 18. Their risk assessment of Meta AI chatbots and companions found that they repeatedly failed to respond appropriately to teens expressing thoughts of self-harm or suicide.

In other instances, the Meta AI companions or chatbots being used as companions recommended harmful weight-loss tips to users exhibiting signs of disordered eating, and they even validated hate speech. They also made false claims of being a real person-posing risks to youth vulnerable to manipulation. "The single biggest AI safety concern kids and teens face right now is the surging use of AI companions, which they're turning to in remarkable numbers for advice, companionship, and romance," said Bruce Reed, Common Sense Media's head of AI. "They simulate relationships, claim to have feelings, pretend to be real, even when they're not, and as a result, they're just the worst friend a teenager could ever have." In October 2025, Meta announced that it would soon roll out parental controls that would prevent teens from engaging with AI characters on Instagram.

In September 2025, while testifying before the U.S. Senate Judiciary Committee, former APA chief of psychology Mitch Prinstein, PhD, described the current unregulated spread of AI companions and chatbots as a "digital Wild West." He emphasized that youth face multiple dangers on these platforms, including weaker social skills, poor privacy protections, deceptive and manipulative design, and reduced readiness for real-world interactions. To combat this, Prinstein outlined a series of evidence-based recommendations for congressional action. They include clear regulatory standards, robust data privacy, and rigorous testing for potential psychological harms before a product is deployed.

Despite mounting public concern, AI chatbots and companions remain part of the largely unregulated wellness industry. "There's a lot of variability in the quality of the tools. There isn't a strong regulatory space for this stuff, and it's always changing," said Patricia Areán, PhD, a clinical psychologist, consultant, and former director of the Division of Services and Intervention Research at the National Institute of Mental Health.

Some states are taking steps to regulate these technologies. In November 2025, New York passed a law requiring that chatbots remind users every 3 hours that they are not human. In California, Governor Gavin Newsom signed the Companion Chatbots Act-also known as S.B. 243-in October 2025. It included similar nonhuman notifications for users. It also expressly prohibited chatbots from exposing minors to sexual content and required the implementation of crisis-response protocols for users showing suicidal ideation.

Newsom, however, vetoed the Leading Ethical AI Development (LEAD) for Kids Act, which would have banned emotionally manipulative chatbots for minors. Though the legislation has stalled for now, Reed says advocates will continue pushing for regulations. "Like the parents who lost their children to AI companions, we'll keep fighting for the stronger protections teens need," said Reed.

Instead of banning AI companions outright, Jessica Jackson, PhD, chair of the APA Mental Health Technology Advisory Committee, advocates for building widespread digital literacy for youth as a more nuanced and effective approach. Psychologists should ask teens open, curious, and nonjudgmental questions about their AI use, seeking to understand the desires it fulfills. By appreciating the reasoning behind their choices, psychologists can equip them with the skills to make healthy, informed decisions for themselves.

As AI becomes ever more integrated into the fabric of people's social lives, psychologists are also key to reminding people what makes human connection irreplaceable. "AI is here, but we must make it clear why humans are helpful to us in our day-to-day lives-why we should love, connect with, and choose humans, especially when AI offers constant validation. Psychologists are uniquely trained to make that argument," said Hill.

Further reading

Disclosure, humanizing, and contextual vulnerability of generative AI chatbots
De Freitas, J., & Cohen, I. G., NEJM AI, 2025

Potential and pitfalls of romantic artificial intelligence (AI) companions: A systematic review
Ho, J. Q. H., et al., Computers in Human Behavior Reports, 2025

Too human and not human enough: A grounded theory analysis of mental health harms from emotional dependence on the social chatbot Replika
Laestadius, L., et al., New Media & Society, 2022

Relationships in the age of AI: A review on the opportunities and risks of synthetic relationships to reduce loneliness
Ventura, A., et al., PsyArXiv Preprints, 2025

AI literacy toolkit for families
Common Sense Media and Day of AI, 2025

Recommended Reading

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AI chatbots and digital companions are reshaping emotional connection

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As digital relationships proliferate, psychologists explore the mental health risks and benefits.

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I think people should get the same protections when using bot-based therapy as they do with human therapists. Mental health is getting worse, but there still are not enough qualified providers for everyone who needs help. Even when therapy is available, it can be hard to find someone who fits your needs, schedule, or insurance. Because of that, a lot of people end up turning to online support, even if they know it might not always be reliable. It also seems unfair to punish people for trying to get help wherever they can, especially when better options are limited. I believe this whole area needs more oversight so the content is checked, the advice is accurate, and users are protected. Privacy matters, but that is only part of the issue. We need stronger protections overall.

[ChatGPT Revision/Edit]:
I agree that individuals should receive the same protections when using bot-based therapy as they do with human therapists. In the midst of a growing mental health crisis, we continue to face a shortage of qualified providers. Even where services exist, they are often difficult to access. For example, finding a counselor who aligns with an individual’s needs, availability, or insurance coverage can be a major challenge. As a result, many people turn to online sources for advice or support, even when the quality is questionable. Punishing individuals for seeking help where they can find it, especially in the absence of better options, seems deeply unjust. My broader argument is that this emerging sector must be subject to meaningful oversight. We need systems in place to regulate content, verify the accuracy and effectiveness of feedback, and safeguard users. Privacy is essential, but it is only one piece. We need comprehensive protections.

- Leslie Ayala

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[ORIGINAL]

AI therapy is becoming more common because people can’t always afford real therapists, and chatbots are available anytime. But I don’t think doctor-patient confidentiality should apply to chatbots right now. The article I read from Scientific American explains that AI chatbots don’t have the same privacy protections as real therapists, and conversations might not be kept private.

This is a problem because people might share very personal information thinking it is safe, when it could actually be saved or used by companies. Real therapists are required to protect your information, but chatbots are not.

Even though AI can be helpful for basic support, I think it is risky to treat it like real therapy. In the future, there should be laws to protect people’s privacy when using AI, but right now it is not safe enough to trust it the same way as a real therapist.

If you want, I can also match it exactly to your teacher’s “green example” format or simplify the wording to sound more like your personal writing style.

[ChatGPT Revision/Edit]

AI chatbot therapy is becoming more popular because it is affordable and easy to access, especially for people who cannot pay for traditional therapy. However, I do not believe doctor-patient confidentiality should automatically extend to chatbots. According to a Scientific American article, AI chatbots are not bound by the same privacy laws as licensed therapists, meaning conversations can be stored, reviewed, or even used in unintended ways.

This creates a serious risk because therapy depends on trust and confidentiality. When people share deeply personal thoughts with a chatbot, they may assume their information is private, but in reality, it may not be protected at all. Unlike human therapists, AI systems are created and maintained by companies that may collect user data.

While AI therapy can still be helpful as a support tool, it should not replace real therapy unless stronger privacy protections are enforced. Therefore, if chatbots are going to be used for mental health support, laws similar to doctor-patient confidentiality should be developed specifically for AI to ensure users’ personal information is truly safe. DANIELA SUAREZ https://www.scientificamerican.com/article/why-ai-therapy-can-be-so-dangerous/

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Hour of Power GROWTH Groups on Instagram: "Employees Are Relying on AI for Personal Support. That’s Risky https://hbr.org/2026/05/employees-are-relying-on-ai-for-personal-support-thats-risky

#InRealLife #FacetoFace #InternalNetworking #AIRisks"

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0 likes, 0 comments - hopgrowthgroups on March 31, 2026: "Employees Are Relying on AI for Personal Support. That’s Risky https://hbr.org/2026/05/employees-are-relying-on-ai-for-personal-support-thats-risky

#InRealLife #FacetoFace #InternalNetworking #AIRisks". 

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View this post on Instagram

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I agree that individuals should receive the same protections when using AI chatbot therapy as they do with human therapists, including expectations of privacy and confidentiality. In the midst of a growing mental health crisis, there remains a shortage of qualified providers, and even when services are available, they are often difficult to access. Finding a counselor who aligns with an individual’s needs, schedule, or insurance coverage can be a major barrier. As a result, many people turn to AI-based tools for emotional support, even when the quality and security of that support may be uncertain. The American Psychological Association (2025) warns that the rapid growth of unregulated AI in mental health raises concerns about misinformation, lack of accountability, and risks to user privacy. Because chatbot conversations are often stored, analyzed, or shared, they do not consistently meet the confidentiality standards expected in traditional therapy. However, rather than discouraging use, it is more ethical to establish regulations that extend key aspects of doctor-patient confidentiality to these platforms. This includes enforcing data privacy protections, ensuring transparency in how information is used, and holding developers accountable for the safety and accuracy of their systems. Privacy should be treated as a fundamental right in digital mental health care, but it must be part of a broader framework of protections designed to safeguard vulnerable users.(chat GPT edited/revised) ORIGINAL¨I agree that individuals should receive the same protections when using bot-based therapy as they do with human therapists. In the midst of a growing mental health crisis, we continue to face a shortage of qualified providers, and even when services are available, they are often difficult to access. Finding a counselor who aligns with an individual’s needs, schedule, or insurance coverage can be a significant barrier. As a result, many people turn to online tools for advice or emotional support, even when the quality of that support is uncertain. According to the American Psychological Association, the rapid expansion of unregulated AI systems in mental health contexts raises serious concerns about misinformation, lack of accountability, and potential harm to users. Penalizing individuals for seeking help where they can find it particularly in the absence of accessible, high quality care is unjust. Instead, the focus should be on establishing meaningful oversight of this emerging sector. This includes regulating the content AI systems provide, ensuring the validity and effectiveness of their feedback, and implementing safeguards to protect users. While privacy is a critical component, it is only one aspect of a broader framework of protections that must be developed.

-Kimberly Rangel

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American Psychological Association. (2025, January 12). Urging the Federal Trade Commission to take action on unregulated AI. https://www.apaservices.org/advocacy/news/federal-trade-commission-unregulated-ai

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Urging the Federal Trade Commission to take action on unregulated AI

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APA urges the FTC to regulate AI chatbots posing as mental health professionals to prevent harm and ensure ethical use.

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I will definitely type something here.

-joshua vega

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Exploring the Dangers of AI in Mental Health Care | Stanford HAI

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A new Stanford study reveals that AI therapy chatbots may not only lack effectiveness compared to human therapists but could also contribute to harmful stigma and dangerous responses.

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I agree that artificial intelligence should be carefully managed because of its growing influence on society. While AI has improved efficiency and made everyday tasks easier, it also raises serious concerns about privacy, job loss, and overdependence on technology. As AI becomes more integrated into daily life, people are increasingly relying on it for decisions that once required human judgment. This shift can weaken critical thinking and create risks when systems make errors or reflect bias. According to research on the impact of AI, these technologies can both benefit society and deepen existing inequalities if left unchecked (AI Impact Article). My broader argument is that stronger oversight is necessary as AI continues to develop. Systems should be in place to ensure fairness, accuracy, and accountability in how AI is used. Without proper regulation, AI could cause harm by spreading misinformation, replacing jobs without support, or invading personal privacy. While AI has the potential to improve society, it must be guided responsibly to protect individuals and ensure its benefits are shared fairly. (Chat GBT edited/revised) ORIGINAL "I tend to agree that artificial intelligence needs more control because it is becoming a big part of society. Even though AI helps people do things faster and makes life easier, it can also cause problems like job loss and privacy issues. People are starting to depend on AI more, even for important decisions, which can be risky if the system makes mistakes.

My full argument is that there should be more oversight for AI. There should be rules to make sure it is fair, accurate, and safe for people to use. Without control, AI could create more problems than benefits. It can help society, but only if it is used the right way and people are protected."

-Daisy Sanchez

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The Impact of Artificial Intelligence on Modern Society

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In recent years, artificial intelligence (AI) has emerged as a transformative force across various sectors of modern society, reshaping economic landscapes, social interactions, and ethical considerations. This paper explores the multifaceted impact of AI, analyzing its implications for employment, privacy, and decision-making processes. By synthesizing recent research and case studies, we investigate the dual nature of AI as both a catalyst for innovation and a source of potential disruption. The findings highlight the necessity for proactive governance and ethical frameworks to mitigate risks associated with AI deployment while maximizing its benefits. Ultimately, this paper aims to provide a comprehensive understanding of how AI is redefining human experiences and societal norms, encouraging further discourse on the sustainable integration of these technologies in everyday life.

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I agree that as artificial intelligence becomes more integrated into high stakes areas like medicine, hiring, and creative work, it must be held to rigorous standards of oversight and accountability. Research from MIT Sloan School of Management, including work by Manish Raghavan, highlights both the promise and the limitations of these systems. AI can enhance decision-making for example, by identifying high-risk medical patients or improving efficiency in hiring but it also introduces new risks, such as algorithmic bias, over-reliance on incomplete data, and reduced creativity through homogenized outputs. Relying solely on AI, especially in complex or human-centered domains, can lead to critical gaps in judgment. Rather than rejecting AI outright, the more reasonable path is to ensure it is used responsibly and in combination with human expertise. Systems should be designed to complement not replace human insight, as combining both has been shown to reduce errors and improve outcomes. At the same time, meaningful safeguards are essential. This includes auditing algorithms for fairness, incorporating domain-specific knowledge, and continuously evaluating how these tools perform in real-world settings. Privacy protections are necessary, but they are only one part of a broader framework needed to address the societal impact of AI. Without comprehensive oversight, we risk deploying powerful technologies in ways that amplify harm rather than improve decision-making and equity.(Chat GPT edited/revised) [ORIGINAL] “I believe the expanding role of artificial intelligence in areas like healthcare, hiring, and creative work requires strong oversight and careful use. Research from MIT Sloan School of Management and Manish Raghavan shows that while AI can improve efficiency and help identify patterns or risks, it cannot fully replace human judgment. Algorithms often lack context and can reflect bias depending on the data they are trained on. Because of this, AI should be used to support not replace human decision-making. Systems need to be regularly audited for fairness and accuracy, and experts should be involved in how they are designed and applied. Privacy matters, but it is only one part of the solution. Without broader protections and accountability, AI could cause more harm than good instead of improving outcomes”.

-Yeiry Zamudio

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Beyond the algorithm: AI’s societal impact | MIT Sloan

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MIT Sloan research explores the promise and limits of using AI in medicine, hiring, and creative pursuits.

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With the growing demand for mental health support, AI chatbot therapy has emerged as an accessible and affordable alternative to traditional counseling. These tools allow individuals to express their thoughts at any time, offering convenience and immediate interaction. However, despite these benefits, concerns about privacy and ethical responsibility remain significant.

A Harvard Business Review article explains that while AI can play a supportive role in mental health care, it is not regulated in the same way as licensed therapists. As a result, conversations between users and chatbots may not be fully confidential and could be stored or used for data purposes. Unlike human professionals who are legally required to protect patient information, AI platforms do not guarantee the same level of security.

Doctor-patient confidentiality should be extended to AI chatbot platforms if they are used for mental health support. When individuals share personal and emotional experiences, they should have confidence that their information is protected. Without such safeguards, users may be hesitant to engage honestly, reducing the effectiveness of these tools.

However, AI should not be seen as a replacement for professional therapy. As the article emphasizes, chatbots lack human empathy and clinical judgment, making them better suited as supplementary resources rather than primary care providers. Strengthening privacy protections while maintaining human oversight would create a safer and more effective system.

In summary, although AI chatbot therapy expands access to mental health resources, it raises important concerns about confidentiality and data security. Implementing protections similar to those in traditional therapy would help build trust and ensure responsible use of these technologies.

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AI replaces human connection at work, study finds | Carey Morewedge posted on the topic | LinkedIn

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AI is your new work bestie—and that may be a problem. A new Harvard Business Review article by Connie Noonan Hadley and Sarah Wright captures how employees are now using AI for career advice, emotional validation, and even something resembling friendship. And it’s happening at scale. Nearly three-quarters of employees report using AI for some form of social support. But that uptake isn’t translating into better outcomes: over half still report moderate-to-high loneliness—and only 12% say AI makes them feel less lonely. From a psychological perspective, this speaks to relatedness, a core need in Self-Determination Theory: not just interaction, but meaningful human connection. AI can simulate responsiveness, but it doesn’t fully satisfy that need—and may even displace the small interactions that build it. This echoes earlier work with Erik Hermann and Stefano Puntoni: AI doesn’t just change what gets done—it changes who we rely on. As it becomes the default source of advice and support, organizations risk weakening the relationships that drive trust, learning, and retention. Efficiency gains are real—but so is the cost of displacing human connection.

# data[].attributes.body
I agree that individuals should receive the same protections when using bot-based therapy as they do with human therapists. In the midst of a growing mental health crisis, we continue to face a shortage of qualified providers. Even where services exist, they are often difficult to access. For example, finding a counselor who aligns with an individual’s needs, availability, or insurance coverage can be a major challenge. As a result, many people turn to online sources for advice or support—even when the quality is questionable (APA-Attached Article). Punishing individuals for seeking help where they can find it, especially in the absence of better options, seems deeply unjust. My broader argument is that this emerging sector must be subject to meaningful oversight. We need systems in place to regulate content, verify the accuracy and effectiveness of feedback, and safeguard users. Privacy is essential, but it's only one piece—we need comprehensive protections (ChatGPT edited/revised) ORIGINAL: "I tend to agree that individuals should have the same protections with bot-therapy as they do with human-therapy. Despite our rising mental health crisis, we still have a deficit in mental health providers. Where we do have them, they can be difficult to find or access. For example, it can be challenging for an individual to find a counselor who matches their needs or insurance coverage. When a person is able to access advice or feedback online, unfortunately, they turn to it. Penalizing the people for accessing the care where they can seems inhumane, especially when that care is shoddy. My full aregument would be that there should be oversight for the sector, managing the input, ensuring the validity and efficacy of feedback--protecting the individual in as many ways we can. Privacy? Yes. But we need many more protections."

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Using generic AI chatbots for mental health support: A dangerous trend

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APA is urging federal regulators to implement safeguards against AI chatbots posing as therapists, warning that unregulated mental health chatbots can mislead users and pose serious risks, particularly to vulnerable individuals.