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Copeland, B. (2024, October 9). artificial intelligence. Encyclopedia Britannica. https://www.britannica.com/technology/artificial-intelligence

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The purpose of this paper is to argue that Artificial intelligence is changing many fields, but issues like AI "hallucinations" and ethical concerns show that we need better controls to make sure these systems are reliable, accurate, and used responsibly.

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Metz, R. (2023, September 13). Hallucinations: Why AI makes stuff up and what’s being done about it. CNET. https://www.cnet.com/tech/hallucinations-why-ai-makes-stuff-up-and-whats-being-done-about-it/

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Mayo Clinic Press Editors. (2024, March 27). AI in healthcare: The future of patient care and health management. Mayo Clinic Press. https://mcpress.mayoclinic.org/healthy-aging/ai-in-healthcare-the-future-of-patient-care-and-health-management/

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(4) Different types of Large Language Models

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(4) Different types of Large Language Models

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Tai, R. H., Bentley, L. R., Xia, X., Sitt, J. M., Fankhauser, S. C., Chicas-Mosier, A. M., & Monteith, B. G. (2024). An Examination of the Use of Large Language Models to Aid Analysis of Textual Data. International Journal of Qualitative Methods, 1–14. https://doi.org/10.1177/16094069241231168

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Holter, S., & El, A. M. (2024). Deconstructing Human‐AI Collaboration: Agency, Interaction, and Adaptation. Computer Graphics Forum, 43(3), 1–12. https://doi.org/10.1111/cgf.15107

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Greenleaf, G., Mowbray, A., & Chung, P. (2018). Building sustainable free legal advisory systems: Experiences from the history of AI & law. Computer Law & Security Review, 34(2), 314–326. https://doi.org/10.1016/j.clsr.2018.02.007

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Salah, M., Alhalbusi, H., Ismail, M. M., & Abdelfattah, F. (2024). Chatting with ChatGPT: decoding the mind of Chatbot users and unveiling the intricate connections between user perception, trust and stereotype perception on self-esteem and psychological well-being. Current Psychology, 43(9), 7843–7858. https://doi.org/10.1007/s12144-023-04989-0

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Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024). Bias and Fairness in Large Language Models: A Survey. Computational Linguistics, 50(3), 1097–1179. https://doi.org/10.1162/coli_a_00524

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Jang, C. (2024). Coping with vulnerability: the effect of trust in AI and privacy-protective behaviour on the use of AI-based services. Behaviour & Information Technology, 43(11), 2388–2400. https://doi.org/10.1080/0144929X.2023.2246590

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Ungless, E. L., Ross, B., & Belle, V. (2023). Potential Pitfalls With Automatic Sentiment Analysis: The Example of Queerphobic Bias. Social Science Computer Review, 41(6), 2211–2229. https://doi.org/10.1177/08944393231152946

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Zhang, J., & Zhang, Z. (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and digital literacy. Journal of Computer Assisted Learning, 40(4), 1871–1885. https://doi.org/10.1111/jcal.12988

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Lee, S., Lee, M., & Lee, S. (2023). What If Artificial Intelligence Become Completely Ambient in Our Daily Lives? Exploring Future Human-AI Interaction through High Fidelity Illustrations. International Journal of Human-Computer Interaction, 39(7), 1371–1389. https://doi.org/10.1080/10447318.2022.2080155

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"Artificial intelligence (AI) refers to the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings."

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"Hallucinations occur when AI systems confidently present incorrect information, even when they have access to the right answer. This happens because AI models sometimes ‘make things up’ when trying to predict the most likely answer."

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"AI has the potential to transform healthcare by improving diagnostic accuracy, optimizing treatment plans, and reducing human errors. Its ability to analyze large datasets makes it a powerful tool for early disease detection and personalized care."

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LLMs help speed up data coding but still need human input to handle complex, nuanced tasks accurately

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The paper reflects on lessons from the DataLex Project to help free legal advice services develop better AI tools.

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The study found that AI coaching services keep users engaged when they offer benefits like usefulness and enjoyment, while high costs reduce perceived value.

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With faster responses, priority access, and advanced GPT-4 features, it helps librarians enhance information access and service delivery.

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This article reviews how LLMs can spread bias and offers ways to measure and reduce it. It provides tools for researchers to manage bias and highlights future challenges.

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AI service usage grows when people trust the technology and feel protected from risks like data leaks. This study, using data from 48,420 South Korean respondents, found that trust in AI and privacy-protective behavior increase usage, while online skills have little effect. The findings highlight the need to improve privacy protections

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Sentiment analysis tools can track moods and preferences but often show bias against marginalized groups. This study found biases in six tools, including models from Google and Amazon, affecting queer identities. It offers advice on choosing tools to reduce biased outcomes.

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This study shows AI can support teaching by improving management, inclusivity, digital skills, personalized methods, and teacher-student relationships, while also noting challenges to its adoption.

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This study used detailed illustrations and an online exhibition to explore user perceptions of future AI technologies. It highlights the need for user-centered design to ensure effective and thoughtful human-AI interactions.

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AI refers to machines performing tasks that typically require human intelligence, like problem-solving and language processing. Its development has advanced with computing power, enabling applications from basic games to complex decision-making systems.

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AI has progressed from early software to advanced robotics and language tools, relying heavily on computing power. However, AI still lacks the adaptability of human intelligence. Tools like the Turing Test assess how well AI mimics human thinking.

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AI hallucinations occur when chatbots generate false information based on patterns, not facts. Companies like OpenAI and Google are reducing these errors, but they persist. Users should verify information and use AI for creative tasks, not facts. Better data, fact-checking, and human oversight, along with regulations like the EU AI Act, help manage these risks.

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AI chatbots sometimes produce false information. Companies are working to reduce these errors, but users should fact-check and use AI mainly for creative purposes. Better data, oversight, and regulations like the EU AI Act are key to managing this issue.

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AI improves healthcare by automating tasks, predicting risks, and saving doctors time. It carries risks like bias and wrong advice, so good rules are important to keep it safe and effective.

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It saves doctors time with tasks like note-taking and quick access to research. AI works alongside doctors, not replacing them. Risks include bias from incomplete data and misleading advice, so proper rules are needed. Mayo Clinic aims to use AI safely to improve diagnoses, treatments, and prevention.

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LLMs like ChatGPT can help with qualitative research by speeding up coding and reducing bias. Though not perfect, LLMs offer reliable support by running multiple analyses. Future improvements could make them even more useful for finding patterns.

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Assist with coding in research, reducing bias and saving time. While not flawless, they offer valuable support, with potential for future improvements

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DataLex helped develop early AI legal systems. This paper suggests using its lessons to create decision-support tools for free legal services and calls for better tools and collaboration with legal information providers.

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Recommends decision-support tools for free legal services, based on lessons from DataLex, and urges collaboration with legal information providers.

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Creates a simple model to explain human-AI collaboration using three parts: agency (who’s in control), interaction (how they work together), and adaptation (how they learn and adjust). Through research and expert feedback, the model helps improve and compare systems. It also highlights challenges like balancing human-AI control and managing complexity.

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The paper explains human-AI collaboration using three ideas: control, interaction, and learning. It offers a model to improve these systems and suggests future research on handling complexity and sharing control.

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This study explored how interacting with ChatGPT affects self-esteem and well-being. It found that trust in ChatGPT, user perceptions, and perceived stereotyping positively impact self-esteem.

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The study shows that trust, perceptions, and stereotyping from ChatGPT influence self-esteem and well-being, with job anxiety playing a role. These insights help improve AI use in mental health and other fields.

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LLMs generate human-like text but can spread biases. This article reviews ways to measure and reduce bias using metrics, datasets, and mitigation techniques. It also identifies challenges and offers guidance for researchers to prevent bias.

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LLMs can spread bias. This study reviews methods to detect and fix it, offering guidance and identifying challenges for future research.

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People are more likely to use AI services if they feel they can control risks like data leaks or misinformation. This study, based on a 2021 survey in South Korea, found that trust in AI and privacy-protective behavior increase AI service use, while online skills have little effect. The findings highlight the need to improve privacy protections and address digital divides when developing AI services and smart government policies.

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AI use grows when people trust the technology and protect their privacy. A survey found that online skills have little effect on usage, emphasizing the importance of privacy protections and policies that build trust.

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Sentiment analysis tools can detect trends like moods and preferences but often show bias against marginalized groups. This study examined six tools and found bias against queer identities, even in Google's and Amazon's models, despite attempts to reduce it. The study offers guidance on choosing tools to limit biased outcomes.

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Sentiment analysis tools can bias results against queer identities. Even Google’s and Amazon’s models show this issue despite efforts to fix it. The study suggests ways to minimize bias when choosing tools.

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AI can enhance teaching by supporting classrooms, personalizing learning, improving digital skills, and strengthening teacher-student relationships. This study, with 202 students and 68 staff, explored both benefits and challenges of AI in education across five areas, including classroom management, inclusivity, and personalized methods.

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AI can improve teaching by personalizing learning and boosting digital skills, but challenges remain. This study highlights both opportunities and obstacles in using AI in education.

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As AI spreads, HCI designers must plan future interactions with users in mind. Since predicting impact is hard, the study used illustrations and an online exhibit to gather user feedback and suggest user-focused AI designs.

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This study used illustrations and user feedback to explore better AI designs for daily life.