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Vad jag plockar med mig från återkoppling Milstolpe 1

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Vad jag plockar med mig från återkoppling Milstolpe 1

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- utveckla filtreringsmöjligheter likt ett Google konto eller liknande som går att "ta med" användaren.
- Väva in samhällets ansvar/skyldigheter att erbjuda samhällsviktiga tjänster på lika villkor.
- att projektet kan bli lite stort för kursen - kan behövas skalas ner en aning
- kolla in visuella hjälpmedel och bildstöd
- betydelsefullt att fokusera på icke-normtypiska användare, exkludering är ett viktigt ämne
- AI kan användas och kanske ska vara huvudfokus?
- Inkludering i det digitala samhället
- Lyfta in resursproblematik, tillgångar och etik.

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Workshop via Zoom 19/9 kritisk design gav idéer till detta projekt

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Workshop via Zoom 19/9 kritisk design gav idéer till detta projekt

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Feedback från workshopen med Renata och Moa

Efter presentation av mitt material så tyckte jag att det var värdefullt att bli påmind av Renata om att min essä i det här skedet är i de 5 steps från discursive, mitt emellan inform och reminder vilket kan behöva smalnas av senare för att vara mer tydlig med syftet av essän. Det nämndes också att det är ofta så att man tänker inte alltid på att inkludera alla användare, man ser till den stora massan men inte alltid att individen inkluderas. Bra punkter om att ofta vill företag att det ska se mer inklusivt ut än vad det är ofta lyftes. Samt att jag med fördel kunde bygga vidare på ett kritisk synsätt, där jag kan lyfta bra och dåligt med metoder för ökad inkludering och utvecklare ofta är alldeles för bekväma i sina tankar och att man utgår från sig själv. Samt att det är något oundvikligt som även jag kommer göra i mitt arbete, även om det finns tankar på inkludering så utgår man alltid från sig själv i slutändan mer eller mindre. Fick värdefulla tips att vara mer tydligt när jag utgår från att "tänka genom att skriva". Där jag fick tips av Moa att ta upp varför jag vill säga det jag vill säga, syftet med det, vem designar man för - välja inriktning - koppla till designprojektet för att rikta in det ännu mer, det är väldigt brett i början lättare att dra ihop säcken om det är kopplat till designprojektet. Vilket var bra påminnelser om att göra detta väldigt tydligt för att inte spreta iväg med arbetet för tidigt utan att hålla sig till ett syfte och istället komma fram till ett tydligt syfte för essän och designarbetet. Jag hade inte heller tänkt att koppla min essä till mitt designprojekt, även om det är lika i sina ämnen då de inkluderar icke-normtypiska användare där den ena fokuserar på social inkludering och den andra på inkludering av användarupplevelser online när det kommer till gränssnitt. Jag fick bra feedback om att jag kanske ska slå ihop dessa två för att kunna koppla de två momenten till varandra.

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Dessa skulle kunna sparas i profiler, så att det går för användaren att byta mellan, AI skulle kunna därefter lära sig när användaren brukar använda vilken profil

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Dessa skulle kunna sparas i profiler, så att det går för användaren att byta mellan, AI skulle kunna därefter lära sig när användaren brukar använda vilken profil

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Färger kan anpassas utifrån ljuskänslighet, läsbarhet, färgblindhet, preferens

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Färger kan anpassas utifrån ljuskänslighet, läsbarhet, färgblindhet, preferens

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En stor mängd preferenser kan matas in i datat

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En stor mängd preferenser kan matas in i datat

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Känns som ett viktigt och stort ämne. Ofta är AI idag baserat på normtypiska beteenden och användare. Genom att lära AI att ta emot data från användaren skulle detta kunna användas för ökad accessability, att användaren kan anpassa vilka färger hen kan se, hur ljuskänlig hen är eller vilka anpassningar hen behöver, detta skulle kunna matas in i en AI för att denna ska kunna identifiera vad som inte stämmer överens med användarens inmatade preferenser.

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https://cacm.acm.org/magazines/2020/6/245157-ai-and-accessibility/fulltext

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According to the World Health Organization, more than one billion people worldwide have disabilities. The field of disability studies defines disability through a social lens; people are disabled to the extent that society creates accessibility barriers. AI technologies offer the possibility of removing many accessibility barriers; for example, computer vision might help people who are blind better sense the visual world, speech recognition and translation technologies might offer real-time captioning for people who are hard of hearing, and new robotic systems might augment the capabilities of people with limited mobility. Considering the needs of users with disabilities can help technologists identify high-impact challenges whose solutions can advance the state of AI for all users; however, ethical challenges such as inclusivity, bias, privacy, error, expectation setting, simulated data, and social acceptability must be considered.

Inclusivity

The inclusivity of AI systems refers to whether they are effective for diverse user populations. Issues regarding a lack of gender and racial diversity in training data are increasingly discussed; however, inclusivity issues with respect to disability are not yet a topic of discourse, though such issues are pervasive. For example, speech recognition systems, which have become popularized by virtual assistants, do not work well for people with speech disabilities such as dysarthria, deaf accent, and other conditions since training data does not typically include samples from such populations. Advances in computer vision have led several groups to propose using such algorithms to identify objects for people who are blind, but today's vision-to-language algorithms have been trained on datasets comprised of images taken by sighted users, limiting their efficacy when applied to images captured by blind users, which tend to have far lower quality. These inclusivity issues threaten to lock people with disabilities out of interacting with the next generation of computing technologies. Proposed methodologies for increasing awareness of the provenance and limitations of datasets are a starting point to increasing dataset transparency. Efforts to directly source data from under-represented user groups, such as the VizWiz dataset, which contains thousands of images and related questions captured by people who are blind, are a step in the right direction.

Bias

Just as AI technologies can amplify existing gender and racial biases in our society, they can also exacerbate disability-based discrimination. Recent research indicates that AI researchers can infer disability status from online data traces; for example, our research showed we could infer whether people are blind by analyzing their Twitter profiles and activity, and researchers have also demonstrated the ability to assess whether people have Parkinson's disease based on their mouse movements while on a search engine homepage. The potential for peoples' disability status to be implicitly revealed through their computing actions means that algorithms could treat users differentially based on inferred disability status, such as the possibility for health insurers to deny coverage or employment advertisements to be targeted to avoid display to people with disabilities. Developing ethical and legal frameworks regarding the application of AI to inferring disability status is a crucial issue.

Privacy

People with rare disabilities may experience greater privacy risks if they contribute data to AI systems or participate in research studies evaluating AI technologies. Past incidents of re-identification of individuals from anonymized datasets, such as the 2006 AOL search data leak, indicate the difficulty of truly anonymizing data. For some small disability populations, privacy-preserving techniques such as k-anonymity may not be effective, creating increased re-identification risk. These increased privacy risks to people with disabilities are amplified by the aforementioned bias issues that people with disabilities may face if their disability status is exposed, and creates an incentive for people with disabilities to withhold their data from research studies, an issue that can further amplify the inclusivity problem of AI systems. Hence, reflection on how current research practices may impact risks of deductive disclosure (for example, Abbot), as well as development of stronger technical and legal privacy frameworks are critical to creating accessible AI technologies.

Error

Many people with disabilities need to trust and rely on the output of an AI system without the ability to verify the output (for example, a person who is blind relying on the output of a computer vision system). Our recent study found that people who were blind were overtrusting of an AI image captioning system, even when the output made little sense; we also found that incorrect output from a computer vision system negatively impacted the understanding of blind users to an extent that could not be corrected even with human assistance. Precision/recall trade-offs for AI systems may need to be recalibrated for vulnerable user populations-for example, the level of confidence a computer vision system must have to offer a label of an image for retrieval by sighted users of an image search engine may be quite different than the level of confidence the same system would need to offer a label of a scene to a blind pedestrian, where errors may have safety consequences. Error metrics output by AI systems must also be intelligible to end users. Translating mathematical confidence values to user-actionable information is an open challenge for AI that is particularly critical to users with disabilities.

Expectation Setting

The language used to describe AI technologies in the scientific literature, the popular press, and advertising materials often creates unreasonable expectations of such systems' current capabilities to lay users. For example, an article announcing that machine translation systems have reached "human parity" for very specific tasks on very specific data sets may lead the general public to make incorrect assumptions about the current state of the art of such AI in the open world. Terminology, demos, articles, and marketing materials that lead the public to misunderstand the capabilities of AI systems are particularly ethically problematic in the case of sensitive user populations, such as people with disabilities, whose quality of life may be fundamentally changed by such advances. In the domain of healthcare, bodies like the FDA regulate the kinds of statements that can be made about the efficacies of various treatments, yet no such regulations exist around promises made regarding the capabilities of apps and algorithms that may impact users' health, well-being, and ability to perform activities of daily living.

Simulated Data

As the need to create inclusive data sets is increasingly recognized, some technologists have used simulation to generate synthetic data; for instance, one approach has been to digitally modify or generate data to create variation not originally present in a dataset. Simulating disability is generally not recommended, since studies have found the data generated by users simulating disabilities (for example, a sighted person wearing a blindfold) is not the same as that from people who are truly disabled (that is, a person who has been blind for years will be much more skilled than a person who has just put on a blindfold). Further, disability simulation has been found to create negative impressions of the capabilities of people with disabilities. However, lack of data is a tension that makes simulation appealing to many technologists. For example, AAC (augmentative and alternative communication) systems that are used by many people with severe speech and motor disabilities are often extremely slow to operate; intelligent language prediction can greatly increase communication bandwidth for AAC users. However, status quo prediction models are often trained on publicly available corpora such as news articles, whose linguistic structure may not represent the typical speech patterns of the target end users. To address this problem, researchers created a simulated AAC speech corpus by asking workers on Amazon's Mechanical Turk service to imagine what they might say if they were disabled; the resulting corpus is skewed toward stereotyped phrases such as "Can I have some water please?" or "Who will drive me to the doctor's office tomorrow?", despite the fact that actual AAC users wish to talk about interests and hobbies beyond their healthcare needs. Creating guidelines and best practices around the use of simulation for developing and testing AI systems for people with disabilities is important for ensuring that systems are not trained on non-representative data. Solutions addressing the aforementioned privacy challenges, which may prevent some people with disabilities from contributing data to AI efforts, may be particularly important for enabling inclusive data set creation that obviates the need for data simulation.

The deployment of AI systems in the open world creates important questions surrounding the social acceptability of new technologies.

Social Acceptability

The deployment of AI systems in the open world creates important questions surrounding the social acceptability of new technologies. Of particular concern are questions regarding technologies' impact on indirect stakeholders, including issues of privacy and fairness. As an example of how privacy concerns influence social acceptability, consider the case of wearable technologies that may be capturing visual and audio data for algorithmic processing, such as Google Glass, which faced a public backlash due to privacy concerns. However, recent research indicates that people are more tolerant of such systems if the technology is being used by a person with disabilities. Technologists, ethicists, and lawmakers must grapple with whether and how rules regarding the use of AI systems might change depending on an end user's disability status, and, if such differential uses of technology are deemed acceptable, how such distinctions could be facilitated in practice.

Conclusion

AI technologies offer great promise for people with disabilities by removing access barriers and enhancing users' capabilities. However, ethical considerations must carefully guide the development, deployment, and discussion around such technologies. These considerations regarding inclusivity, bias, privacy, error, expectation setting, simulated data, and social acceptability apply to all users, but are particularly nuanced and salient when considering the large potential benefits and large potential risks of AI systems for people with disabilities. While legal regulation may address some of these considerations in the future, it is unlikely to keep pace with the changing technological landscape. Educating our next generation of innovators is of paramount importance; emerging ethics curricula for computer science students should include content such as sociological concepts from the field of disability studies and inclusive design approaches from HCI. It is also vital to ensure that people with disabilities have a voice in technological innovation by making our educational institutions and workplaces more inclusive so as to increase the representation of people with disabilities among computer science professionals. As technologists, it is our responsibility to proactively address these issues in order to ensure people with disabilities are not left behind by the AI revolution.

References

3. Gebru, T. et al. Datasheets for datasets. arXiv, v3, (July 2018); https://arxiv.org/abs/1803.09010

8. Lipton, Z.C. and Steinhardt, J. Troubling trends in machine learning scholarship. ICML 2018: Machine Learning Debates. (2018); https://arxiv.org/pdf/1807.03341.pdf

16. Trewin, S. AI fairness for people with disabilities: Point of view. arXiv, 2018; preprint arXiv:1811.10670.

Author

Meredith Ringel Morris ( merrie@microsoft.com) is a Senior Principal Researcher at Microsoft Research, where she leads the Ability team in conducting research that combines advances in HCI and AI to address the technology needs of people with disabilities. She is also an affiliate Professor in The Paul G. Allen School of Computer Science & Engineering and in The Information School at the University of Washington, Seattle, WA, USA.

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According to the World Health Organization, more than one billion people worldwide have disabilities. The field of disability studies defines disability through a social lens; people are disabled to the extent that society creates accessibility barriers.

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https://www.forbes.com/sites/forbesbusinesscouncil/2021/02/09/artificial-intelligence-for-good-how-ai-is-helping-humanity/

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Founder and CEO, Analytics Insight, providing organizations with strategic insights on disruptive technologies.
Artificial intelligence (AI) is considered one of the most revolutionary developments in human history, and the world has already witnessed its transformative capabilities. Not surprisingly, AI-based innovations are powering some of the most cutting-edge solutions we use in our daily lives.

Today, AI empowers organizations, governments and communities to build a high-performing ecosystem to serve the entire world. Its profound impact on human lives is solving some of the most critical challenges faced by society. Here are a few innovations for social causes that I find most notable.

Developing New Drugs: The healthcare industry is ripe with disruptive applications of AI, including the discovery and development of new drugs. AI and machine learning have been used to identify potential molecules by leveraging a large volume of data. Pharmaceutical companies use predictive analytics to discover these molecule candidates and optimize them with several rounds of iteration to select the best one for drug manufacturing.

Reporting Sexual Harassment: Artificial intelligence offers new ways of reporting gender-based violence, child sex abuse and more. AI programs are being designed to monitor internal communications, such as corporate documents, emails and chat, for inappropriate content. Various applications and platforms have been developed to help victims share their experiences of sexual harassment and abuse along with the time and location these events took place.

Combatting Human Trafficking: Human trafficking is a serious crime against humanity and a threat to global security. Traffickers often use the internet to place advertisements to lure potential victims. Artificial intelligence tools and computer vision algorithms scrape images from different websites used by traffickers and label objects in images to search and review suspect advertisements. Additionally, these tools analyze data from the advertisements and websites to identify the potential victims of human trafficking and alert authorities before the crime.
Investing In AI For Good
Optimizing Renewable Energy Generation: Artificial intelligence in collaboration with other technologies, such as the Internet of Things (IoT), cloud computing and big data analytics, has significantly transformed the renewable energy sector. AI programs have the ability to combine weather data and sensors to optimize, predict and manage energy consumption across different sectors. AI-based accurate predictions result in increased dispatch efficiency and reducing the operating reserves needed.

Helping People With Disabilities: Artificial intelligence has also assisted people living independently with disabilities. Voice-assisted AI is one of the major breakthroughs, particularly for those who are visually impaired. It helps them communicate with others using smart devices and describe their surroundings. Tools like this can significantly help in overcoming daily obstacles for those with disabilities.
Bottom Line
While the adoption of AI technologies is increasing, challenges remain. I've found that some of the major challenges faced by organizations developing AI solutions for social good include the fear of risk; defining how to measure the value the solution will bring; an incomplete understanding of AI; the high cost of technology; and regulatory, ethical and security concerns. However, organizations and institutions can overcome them by investing in advanced research, human capital and infrastructure, and encouraging AI literacy in society.

Organizations planning to invest in advanced AI research or implementing AI for social good must actively collaborate with research institutions and government bodies to apply their AI solutions for real-world impact. Moreover, workshops and forums are some of the best platforms for organizations to gather the insights they need. These platforms can be used to understand whether an organization's solutions are the right fit to solve challenges for social good.

Artificial intelligence has enormous potential to serve society, bringing more radical innovations for humans in the future. Its problem-solving ability could help people and communities around the world by solving today's toughest challenges. With sensible use of AI, we should continue to see a wide scope of AI applications and new developments for social good.

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Council Post: Artificial Intelligence For Good: How AI Is Helping Humanity

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AI empowers organizations, governments and communities to build a high-performing ecosystem, and its profound impact on human lives is solving some of the most critical challenges faced by society.

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https://www.mckinsey.com/featured-insights/artificial-intelligence/applying-artificial-intelligence-for-social-good

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Artificial intelligence (AI) has the potential to help tackle some of the world's most challenging social problems. To analyze potential applications for social good, we compiled a library of about 160 AI social-impact use cases. They suggest that existing capabilities could contribute to tackling cases across all 17 of the UN's sustainable-development goals, potentially helping hundreds of millions of people in both advanced and emerging countries.

Real-life examples of AI are already being applied in about one-third of these use cases, albeit in relatively small tests. They range from diagnosing cancer to helping blind people navigate their surroundings, identifying victims of online sexual exploitation, and aiding disaster-relief efforts (such as the flooding that followed Hurricane Harvey in 2017). AI is only part of a much broader tool kit of measures that can be used to tackle societal issues, however. For now, issues such as data accessibility and shortages of AI talent constrain its application for social good.

This article is a condensed version of our discussion paper, Notes from the AI frontier: Applying AI for social good (PDF-3MB). It looks at domains of social good where AI could be applied, and the most pertinent types of AI capabilities, as well as the bottlenecks and risks that must be overcome and mitigated if AI is to scale up and realize its full potential for social impact. The article is divided into five sections:

1. Mapping AI use cases to domains of social good

2. AI capabilities that can be used for social good

3. Overcoming bottlenecks, especially around data and talent

4. Risks to be managed

5. Scaling up the use of AI for social good

Mapping AI use cases to domains of social good

For the purposes of this research, we defined AI as deep learning. We grouped use cases into ten social-impact domains based on taxonomies in use among social-sector organizations, such as the AI for Good Foundation and the World Bank. Each use case highlights a type of meaningful problem that can be solved by one or more AI capability. The cost of human suffering, and the value of alleviating it, are impossible to gauge and compare. Nonetheless, employing usage frequency as a proxy, we measure the potential impact of different AI capabilities.

For about one-third of the use cases in our library, we identified an actual AI deployment (Exhibit 1). Since many of these solutions are small test cases to determine feasibility, their functionality and scope of deployment often suggest that additional potential could be captured. For three-quarters of our use cases, we have seen solutions deployed that use some level of advanced analytics; most of these use cases, although not all, would further benefit from the use of AI techniques. Our library is not exhaustive and continues to evolve, along with the capabilities of AI.

Crisis response

These are specific crisis-related challenges, such as responses to natural and human-made disasters in search and rescue missions, as well as the outbreak of disease. Examples include using AI on satellite data to map and predict the progression of wildfires and thereby optimize the response of firefighters. Drones with AI capabilities can also be used to find missing persons in wilderness areas.

Economic empowerment

With an emphasis on currently vulnerable populations, these domains involve opening access to economic resources and opportunities, including jobs, the development of skills, and market information. For example, AI can be used to detect plant damage early through low-altitude sensors, including smartphones and drones, to improve yields for small farms.

Educational challenges

These include maximizing student achievement and improving teachers' productivity. For example, adaptive-learning technology could base recommended content to students on past success and engagement with the material.

Environmental challenges

Sustaining biodiversity and combating the depletion of natural resources, pollution, and climate change are challenges in this domain. (See Exhibit 2 for an illustration on how AI can be used to catch wildlife poachers.) The Rainforest Connection, a Bay Area nonprofit, uses AI tools such as Google's TensorFlow in conservancy efforts across the world. Its platform can detect illegal logging in vulnerable forest areas by analyzing audio-sensor data.

Equality and inclusion

Addressing challenges to equality, inclusion, and self-determination (such as reducing or eliminating bias based on race, sexual orientation, religion, citizenship, and disabilities) are issues in this domain. One use case, based on work by Affectiva, which was spun out of the MIT Media Lab, and Autism Glass, a Stanford research project, involves using AI to automate the recognition of emotions and to provide social cues to help individuals along the autism spectrum interact in social environments.

Health and hunger

This domain addresses health and hunger challenges, including early-stage diagnosis and optimized food distribution. Researchers at the University of Heidelberg and Stanford University have created a disease-detection AI system-using the visual diagnosis of natural images, such as images of skin lesions to determine if they are cancerous-that outperformed professional dermatologists. AI-enabled wearable devices can already detect people with potential early signs of diabetes with 85 percent accuracy by analyzing heart-rate sensor data. These devices, if sufficiently affordable, could help more than 400 million people around the world afflicted by the disease.

Information verification and validation

This domain concerns the challenge of facilitating the provision, validation, and recommendation of helpful, valuable, and reliable information to all. It focuses on filtering or counteracting misleading and distorted content, including false and polarizing information disseminated through the relatively new channels of the internet and social media. Such content can have severely negative consequences, including the manipulation of election results or even mob killings, in India and Mexico, triggered by the dissemination of false news via messaging applications. Use cases in this domain include actively presenting opposing views to ideologically isolated pockets in social media.

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Infrastructure management

This domain includes infrastructure challenges that could promote the public good in the categories of energy, water and waste management, transportation, real estate, and urban planning. For example, traffic-light networks can be optimized using real-time traffic camera data and Internet of Things (IoT) sensors to maximize vehicle throughput. AI can also be used to schedule predictive maintenance of public transportation systems, such as trains and public infrastructure (including bridges), to identify potentially malfunctioning components.

Public and social-sector management

Initiatives related to efficiency and the effective management of public- and social-sector entities, including strong institutions, transparency, and financial management, are included in this domain. For example, AI can be used to identify tax fraud using alternative data such as browsing data, retail data, or payments history.

Security and justice

This domain involves challenges in society such as preventing crime and other physical dangers, as well as tracking criminals and mitigating bias in police forces. It focuses on security, policing, and criminal-justice issues as a unique category, rather than as part of public-sector management. An example is using AI and data from IoT devices to create solutions that help firefighters determine safe paths through burning buildings.

Our use-case domains cover all 17 of the UN's Sustainable Development Goals

The United Nations' Sustainable Development Goals (SDGs) are among the best-known and most frequently cited societal challenges, and our use cases map to all 17 of the goals, supporting some aspect of each one (Exhibit 3). Our use-case library does not rest on the taxonomy of the SDGs, because their goals, unlike ours, are not directly related to AI usage; about 20 cases in our library do not map to the SDGs at all. The chart should not be read as a comprehensive evaluation of AI's potential for each SDG; if an SDG has a low number of cases, that reflects our library rather than AI's applicability to that SDG.

AI capabilities that can be used for social good

We identified 18 AI capabilities that could be used to benefit society. Fourteen of them fall into three major categories: computer vision, natural-language processing, and speech and audio processing. The remaining four, which we treated as stand-alone capabilities, include three AI capabilities: reinforcement learning, content generation, and structured deep learning. We also included a category for analytics techniques.

When we subsequently mapped these capabilities to domains (aggregating use cases) in a heat map, we found some clear patterns (Exhibit 4).

Image classification and object detection are powerful computer-vision capabilities

Within computer vision, the specific capabilities of image classification and object detection stand out for their potential applications for social good. These capabilities are often used together-for example, when drones need computer vision to navigate a complex forest environment for search-and-rescue purposes. In this case, image classification may be used to distinguish normal ground cover from footpaths, thereby guiding the drone's directional navigation, while object detection helps circumvent obstacles such as trees.

Some of these use cases consist of tasks a human being could potentially accomplish on an individual level, but the required number of instances is so large that it exceeds human capacity (for example, finding flooded or unusable roads across a large area after a hurricane). In other cases, an AI system can be more accurate than humans, often by processing more information (for example, the early identification of plant diseases to prevent infection of the entire crop).

Computer-vision capabilities such as the identification of people, face detection, and emotion recognition are relevant only in select domains and use cases, including crisis response, security, equality, and education, but where they are relevant, their impact is great. In these use cases, the common theme is the need to identify individuals, most easily accomplished through the analysis of images. An example of such a use case would be taking advantage of face detection on surveillance footage to detect the presence of intruders in a specific area. (Face detection applications detect the presence of people in an image or video frame and should not be confused with facial recognition, which is used to identify individuals by their features.)

Natural-language processing

Some aspects of natural-language processing, including sentiment analysis, language translation, and language understanding, also stand out as applicable to a wide range of domains and use cases. Natural-language processing is most useful in domains where information is commonly stored in unstructured textual form, such as incident reports, health records, newspaper articles, and SMS messages.

As with methods based on computer vision, in some cases a human can probably perform a task with greater accuracy than a trained machine-learning model can. Nonetheless, the speed of "good enough" automated systems can enable meaningful scale efficiencies-for example, providing automated answers to questions that citizens may ask through email. In other cases, especially those that require processing and analyzing vast amounts of information quickly, AI models could outperform humans. An illustrative example could include monitoring the outbreak of disease by analyzing tweets sent in multiple local languages.

Some capabilities, or combination of capabilities, can give the target population opportunities that would not otherwise exist, especially for use cases that involve understanding the natural environment through the interpretation of vision, sound, and speech. An example is the use of AI to help educate children who are on the autism spectrum. Although professional therapists have proved effective in creating behavioral-learning plans for children with autism spectrum disorder (ASD), waitlists for therapy are long. AI tools, primarily using emotion recognition and face detection, can increase access to such educational opportunities by providing cues to help children identify and ultimately learn facial expressions among their family members and friends.

Structured deep learning also may have social-benefit applications

A third category of AI capabilities with social-good applications is structured deep learning to analyze traditional tabular data sets. It can help solve problems ranging from tax fraud (using tax-return data) to finding otherwise hard to discover patterns of insights in electronic health records.

Structured deep learning (SDL) has been gaining momentum in the commercial sector in recent years. We expect to see that trend spill over into solutions for social-good use cases, particularly given the abundance of tabular data in the public and social sectors. By automating aspects of basic feature engineering, SDL solutions reduce the need either for domain expertise or an innate understanding of the data and which aspects of the data are important.

Advanced analytics can be a more time- and cost-effective solution than AI for some use cases

Some of the use cases in our library are better suited to traditional analytics techniques, which are easier to create, than to AI. Moreover, for certain tasks, other analytical techniques can be more suitable than deep learning. For example, in cases where there is a premium on explainability, decision tree-based models can often be more easily understood by humans. In Flint, Michigan, machine learning (sometimes referred to as AI, although for this research we defined AI more narrowly as deep learning) is being used to predict houses that may still have lead water pipes (Exhibit 5).

Overcoming bottlenecks, especially for data and talent

While the social impact of AI is potentially very large, certain bottlenecks must be overcome if even some of that potential is to be realized. In all, we identified 18 potential bottlenecks through interviews with social-domain experts and with AI researchers and practitioners. We grouped these bottlenecks in four categories of importance.

The most significant bottlenecks are data accessibility, a shortage of talent to develop AI solutions, and "last-mile" implementation challenges (Exhibit 6).

Data needed for social-impact uses may not be easily accessible

Data accessibility remains a significant challenge. Resolving it will require a willingness, by both private- and public-sector organizations, to make data available. Much of the data essential or useful for social-good applications are in private hands or in public institutions that might not be willing to share their data. These data owners include telecommunications and satellite companies; social-media platforms; financial institutions (for details such as credit histories); hospitals, doctors, and other health providers (medical information); and governments (including tax information for private individuals). Social entrepreneurs and nongovernmental organizations (NGOs) may have difficulty accessing these data sets because of regulations on data use, privacy concerns, and bureaucratic inertia. The data may also have business value and could be commercially available for purchase. Given the challenges of distinguishing between social use and commercial use, the price may be too high for NGOs and others wanting to deploy the data for societal benefits.

The expert AI talent needed to develop and train AI models is in short supply

Just over half of the use cases in our library can leverage solutions created by people with less AI experience. The remaining use cases are more complex as a result of a combination of factors, which vary with the specific case. These need high-level AI expertise-people who may have PhDs or considerable experience with the technologies. Such people are in short supply.

For the first use cases requiring less AI expertise, the needed solution builders are data scientists or software developers with AI experience but not necessarily high-level expertise. Most of these use cases are less complex models that rely on single modes of data input.

The complexity of problems increases significantly when use cases require several AI capabilities to work together cohesively, as well as multiple different data-type inputs. Progress in developing solutions for these cases will thus require high-level talent, for which demand far outstrips supply and competition is fierce.

'Last-mile' implementation challenges are also a significant bottleneck for AI deployment for social good

Even when high-level AI expertise is not required, NGOs and other social-sector organizations can face technical problems, over time, deploying and sustaining AI models that require continued access to some level of AI-related skills. The talent required could range from engineers who can maintain or improve the models to data scientists who can extract meaningful output from them. Handoffs fail when providers of solutions implement them and then disappear without ensuring that a sustainable plan is in place.

Organizations may also have difficulty interpreting the results of an AI model. Even if a model achieves a desired level of accuracy on test data, new or unanticipated failure cases often appear in real-life scenarios. An understanding of how the solution works may require a data scientist or "translator." Without one, the NGO or other implementing organization may trust the model's results too much: most AI models cannot perform accurately all the time, and many are described as "brittle" (that is, they fail when their inputs stray in specific ways from the data sets on which they were trained).

Risks to be managed

AI tools and techniques can be misused by authorities and others who have access to them, so principles for their use must be established. AI solutions can also unintentionally harm the very people they are supposed to help.

An analysis of our use-case library found that four main categories of risk are particularly relevant when AI solutions are leveraged for social good: bias and fairness, privacy, safe use and security, and "explainability" (the ability to identify the feature or data set that leads to a particular decision or prediction).

Bias in AI may perpetuate and aggravate existing prejudices and social inequalities, affecting already-vulnerable populations and amplifying existing cultural prejudices. Bias of this kind may come about through problematic historical data, including unrepresentative or inaccurate sample sizes. For example, AI-based risk scoring for criminal-justice purposes may be trained on historical criminal data that include biases (among other things, African Americans may be unfairly labeled as high risk). As a result, AI risk scores would perpetuate this bias. Some AI applications already show large disparities in accuracy depending on the data used to train algorithms; for example, examination of facial-analysis software shows an error rate of 0.8 percent for light-skinned men; for dark-skinned women, the error rate is 34.7 percent.

One key source of bias can be poor data quality-for example, when data on past employment records are used to identify future candidates. An AI-powered recruiting tool used by one tech company was abandoned recently after several years of trials. It appeared to show systematic bias against women, which resulted from patterns in training data from years of hiring history. To counteract such biases, skilled and diverse data-science teams should take into account potential issues in the training data or sample intelligently from them.

Breaching the privacy of personal information could cause harm

Privacy concerns concerning sensitive personal data are already rife for AI. The ability to assuage these concerns could help speed public acceptance of its widespread use by profit-making and nonprofit organizations alike. The risk is that financial, tax, health, and similar records could become accessible through porous AI systems to people without a legitimate need to access them. That would cause embarrassment and, potentially, harm.

Safe use and security are essential for societal good uses of AI

Ensuring that AI applications are used safely and responsibly is an essential prerequisite for their widespread deployment for societal aims. Seeking to further social good with dangerous technologies would contradict the core mission and could also spark a backlash, given the potentially large number of people involved. For technologies that could affect life and well-being, it will be important to have safety mechanisms in place, including compliance with existing laws and regulations. For example, if AI misdiagnoses patients in hospitals that do not have a safety mechanism in place-particularly if these systems are directly connected to treatment processes-the outcomes could be catastrophic. The framework for accountability and liability for harm done by AI is still evolving.

Decisions made by complex AI models will need to become more readily explainable

Explaining in human terms the results from large, complex AI models remains one of the key challenges to acceptance by users and regulatory authorities. Opening the AI "black box" to show how decisions are made, as well as which factors, features, and data sets are decisive and which are not, will be important for the social use of AI. That will be especially true for stakeholders such as NGOs, which will require a basic level of transparency and will probably want to give clear explanations of the decisions they make. Explainability is especially important for use cases relating to decision making about individuals and, in particular, for cases related to justice and criminal identification, since an accused person must be able to appeal a decision in a meaningful way.

Mitigating risks

Effective mitigation strategies typically involve "human in the loop" interventions: humans are involved in the decision or analysis loop to validate models and double-check results from AI solutions. Such interventions may call for cross-functional teams, including domain experts, engineers, product managers, user-experience researchers, legal professionals, and others, to flag and assess possible unintended consequences.

Human analysis of the data used to train models may be able to identify issues such as bias and lack of representation. Fairness and security "red teams" could carry out solution tests, and in some cases third parties could be brought in to test solutions by using an adversarial approach. To mitigate this kind of bias, university researchers have demonstrated methods such as sampling the data with an understanding of their inherent bias and creating synthetic data sets based on known statistics.

Guardrails to prevent users from blindly trusting AI can be put in place. In medicine, for example, misdiagnoses can be devastating to patients. The problems include false-positive results that cause distress; wrong or unnecessary treatments or surgeries; or, even worse, false negatives, so that patients do not get the correct diagnosis until a disease has reached the terminal stage.

Technology may find some solutions to these challenges, including explainability. For example, nascent approaches to the transparency of models include local-interpretable-model-agnostic (LIME) explanations, which attempt to identify those parts of input data a trained model relies on most to make predictions.

Scaling up the use of AI for social good

As with any technology deployment for social good, the scaling up and successful application of AI will depend on the willingness of a large group of stakeholders-including collectors and generators of data, as well as governments and NGOs-to engage. These are still the early days of AI's deployment for social good, and considerable progress will be needed before the vast potential becomes a reality. Public- and private-sector players all have a role to play.

Improving data accessibility for social-impact cases

A wide range of stakeholders owns, controls, collects, or generates the data that could be deployed for AI solutions. Governments are among the most significant collectors of information, which can include tax, health, and education data. Massive volumes of data are also collected by private companies-including satellite operators, telecommunications firms, utilities, and technology companies that run digital platforms, as well as social-media sites and search operations. These data sets may contain highly confidential personal information that cannot be shared without being anonymized. But private operators may also commercialize their data sets, which may therefore be unavailable for pro-bono social-good cases.

Overcoming this accessibility challenge will probably require a global call to action to record data and make it more readily available for well-defined societal initiatives.

Data collectors and generators will need to be encouraged-and possibly mandated-to open access to subsets of their data when that could be in the clear public interest. This is already starting to happen in some areas. For example, many satellite data companies participate in the International Charter on Space and Major Disasters, which commits them to open access to satellite data during emergencies, such as the September 2018 tsunami in Indonesia and Hurricane Michael, which hit the US East Coast in October 2018.

Notes from the AI frontier: Applying AI for social good

Close collaboration between NGOs and data collectors and generators could also help facilitate this push to make data more accessible. Funding will be required from governments and foundations for initiatives to record and store data that could be used for social ends.

Even if the data are accessible, using them presents challenges. Continued investment will be needed to support high-quality data labeling. And multiple stakeholders will have to commit themselves to store data so that they can be accessed in a coordinated way and to use the same data-recording standards where possible to ensure seamless interoperability.

Issues of data quality and of potential bias and fairness will also have to be addressed if the data are to be deployed usefully. Transparency will be a key for bias and fairness. A deep understanding of the data, their provenance, and their characteristics must be captured, so that others using the data set understand the potential flaws.

All this is likely to require collaboration among companies, governments, and NGOs to set up regular data forums, in each industry, to work on the availability and accessibility of data and on connectivity issues. Ideally, these stakeholders would set global industry standards and collaborate closely on use cases to ensure that implementation becomes feasible.

Overcoming AI talent shortages is essential for implementing AI-based solutions for social impact

The long-term solution to the talent challenges we have identified will be to recruit more students to major in computer science and specialize in AI. That could be spurred by significant increases in funding-both grants and scholarships-for tertiary education and for PhDs in AI-related fields. Given the high salaries AI expertise commands today, the market may react with a surge in demand for such an education, although the advanced math skills needed could discourage many people.

Sustaining or even increasing current educational opportunities would be helpful. These opportunities include "AI residencies"-one-year training programs at corporate research labs-and shorter-term AI "boot camps" and academies for midcareer professionals. An advanced degree typically is not required for these programs, which can train participants in the practice of AI research without requiring them to spend years in a PhD program.

Given the shortage of experienced AI professionals in the social sector, companies with AI talent could play a major role in focusing more effort on AI solutions that have a social impact. For example, they could encourage employees to volunteer and support or coach noncommercial organizations that want to adopt, deploy, and sustain high-impact AI solutions. Companies and universities with AI talent could also allocate some of their research capacity to new social-benefit AI capabilities or solutions that cannot otherwise attract people with the requisite skills.

Overcoming the shortage of talent that can manage AI implementations will probably require governments and educational providers to work with companies and social-sector organizations to develop more free or low-cost online training courses. Foundations could provide funding for such initiatives.

Task forces of tech and business translators from governments, corporations, and social organizations, as well as freelancers, could be established to help teach NGOs about AI through relatable case studies. Beyond coaching, these task forces could help NGOs scope potential projects, support deployment, and plan sustainable road maps.

From the modest library of use cases that we have begun to compile, we can already see tremendous potential for using AI to address the world's most important challenges. While that potential is impressive, turning it into reality on the scale it deserves will require focus, collaboration, goodwill, funding, and a determination among many stakeholders to work for the benefit of society. We are only just setting out on this journey. Reaching the destination will be a step-by-step process of confronting barriers and obstacles. We can see the moon, but getting there will require more work and a solid conviction that the goal is worth all the effort-for the sake of everyone.

About the author(s)
Michael Chui is a partner and James Manyika is chairman and a director of the McKinsey Global Institute. Martin Harrysson and Roger Roberts are partners in McKinsey's Silicon Valley office, where Rita Chung is a consultant. Pieter Nel is a specialist in the New York office; Ashley van Heteren is an expert associate principal in the Amsterdam office.

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Applying artificial intelligence for social good

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Artificial intelligence (AI) has the potential to help tackle some of the world's most challenging social problems. To analyze potential applications for social good, we compiled a library of about 160 AI social-impact use cases.

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https://www.weforum.org/agenda/2022/05/how-ai-can-help-the-world-fight-wildfires/

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How AI can help the world fight wildfires

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AI algorithms and end-to-end fire management systems are powerful tools for fighting wildfires. Global collaboration on such solutions could cut wildfire risk.

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Studentdesignprojekt kan ha olika typ av projektdesign och fokus. En möjlig kategorisering är enligt Hertzum (2014):

Analysprojekt. Fokus på analys av existerande situation.

Konstruktionsprojekt. Fokus på konstruktion utifrån tekniska möjligheter.

Processprojekt. Fokus på designprocessen.

Visionsprojekt. Fokus på vision av ny situation.

Varje typ av projektdesign har sina för- och nackdelar. En styrka inom ett område kommer på bekostnad av svagheter i andra viktiga områden. Dock, att fokusera på samtliga fyra områden i ett studentprojekt är orealistiskt. Det är därför viktigt att vara medveten om detta, reflektera kring det, och göra avvägningar i projektdesignen, genom att ställa följande fyra frågor som ingångar i projektplaneringen:

Vad är desiderata (önskningar, etiska ställningstaganden, grundläggande behov), mål, problem. Och för vem? (Analys)

Vilka teknologier finns tillgängliga som byggblock? (Konstruktion)

Hur kan förändring/design åstadkommas? (Process)

Vad är möjliga idéer till lösningar (Vision)

Det är viktigt att du påbörjar ditt designprojekt så snart som möjligt och inte drabbas av studentsyndromet (Wikipedia, Student syndrome 2021). Som i allt designarbete kan och förväntas rent av ramen och inriktningen ändras, men som en godtycklig tumregel bör det senast 20-33% in i projekttiden vara någorlunda klart vad som ska designas och/eller beforskas.

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Hertzum, Morten (2014). Project Designs for Student Design Projects 

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Hertzum, Morten (2014). Project Designs for Student Design Projects 

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https://www.semanticscholar.org/paper/Project-Designs-for-Student-Design-Projects-Simonsen-Svabo/08a3362d9887a76d4f54fbe3a17edeab8cabda9a

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[PDF] Project Designs for Student Design Projects | Semantic Scholar

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This chapter contains sections titled: 1 Introduction, 2 A Simplified Model of Design Projects, 3 The Analysis Project, 4 The Construction Project, 5 The Process Project, 6 The Vision Project, 7 The Four-Question Method, 8 Discussion, 9 Conclusion, References

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Goldschmidt, G. & Matthews, B. (2022). Formulating design research questions: A framework

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Goldschmidt, G. & Matthews, B. (2022). Formulating design research questions: A framework

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https://www.sciencedirect.com/science/article/abs/pii/S0142694X21000739

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Highlights

* We propose a set of six characteristics of research questions.

* Effective empirical design research questions are relevant, interesting, and novel.

* Further characteristics pertain to method: appropriate, feasible and ethical.

* Research questions have implications for other dimensions of the study.

* Quality questions open new lines of inquiry for the field.

This research note addresses the formulation of questions in design research. On the premise that well formulated research questions have far-reaching implications for other aspects of the development and impact of research, articulating quality research questions is an issue of the highest concern. In this note we propose a framework for the formulation of research questions, adapted from clinical research and tailored to empirical design research. The framework, RIN. AFE, pertains to criteria for effective research questions: on the substance side, Relevant, Interesting and Novel; on the methodological execution side, Appropriate, Feasible and Ethical. The application of this framework is demonstrated through an analysis of an award-winning Design Studies paper.

View full text

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Formulating design research questions: A framework

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We propose a set of six characteristics of research questions. * Effective empirical design research questions are relevant, interesting, and novel. * Further characteristics pertain to method: appropriate, feasible and ethical. * Research questions have implications for other dimensions of the study. * Quality questions open new lines of inquiry for the field.

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https://www.tellart.com/projects/designnonfiction

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Historically, design has transformed and generated new disciplines during times of fast technological change. Through conversations led by Tellart, this project explores and documents transformations in design between the Dotcom Crash and the rise of machine intelligence. Through reflections on key projects from this period and interviews with a community of today's top design practitioners, Design Nonfiction explores the future of design practice.

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Crawford, Kate & Joler, Vladan. (2018). Anatomy of an AI System 

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Crawford, Kate & Joler, Vladan. (2018). Anatomy of an AI System 

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https://anatomyof.ai/

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Anatomy of an AI System - The Amazon Echo as an anatomical map of human labor, data and planetary resources. By Kate Crawford and Vladan Joler (2018)

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Cannucciari, Chris (2019). Machine Learning: Living in the Age of AI 

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Cannucciari, Chris (2019). Machine Learning: Living in the Age of AI 

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https://www.wired.com/video/watch/machine-learning-living-in-the-age-of-ai-film

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Hebron, Patrick (2016). Machine Learning for Designers 

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Hebron, Patrick (2016). Machine Learning for Designers 

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https://www.oreilly.com/library/view/machine-learning-for/9781491971444/

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Machine learning is no longer just a tool for data scientists. By taking advantage of recent advances in this technology, UI and UX designers can find ways to better engage with and understand their users. This O'Reilly report not only introduces you to contemporary machine learning systems, but also provides a conceptual framework to help you integrate machine-learning capabilities into your user-facing designs.

Using tangible, real-world examples, author Patrick Hebron explains how machine-learning applications can affect the way you design websites, mobile applications, and other software. You'll learn how recent advancements in machine learning can radically enhance software capabilities through natural language processing, image recognition, content personalization, and behavior prediction.

This report explains how to:

Leverage machine-generated user insights to provide a more personalized customer or user experience

Spot opportunities for the integration of machine-learning capabilities into existing designs and platforms

Choose the right machine-learning platforms or services

Design for the probabilistic and often imprecise nature of machine-generated data

Stay up to date with advancements in the field and spot emerging opportunities for machine learning-aided design

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Machine learning is no longer just a tool for data scientists. By taking advantage of recent advances in this technology, UI and UX designers can find ways to better engage with and understand their users. This O'Reilly report not only introduces you to contemporary machine learning systems, but also provides a conceptual framework to help you integrate machine-learning capabilities into your user-facing designs.

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Buxton (2010). Sketching User Experiences: Getting the Design Right and the Right Design 

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Buxton (2010). Sketching User Experiences: Getting the Design Right and the Right Design 

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https://go.exlibris.link/WnLPmnmg

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Biblioteket byter system för hylluppställning.

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Legg, Shane & Hutter, Marcus (2007). A Collection of Definitions of Intelligence 

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Legg, Shane & Hutter, Marcus (2007). A Collection of Definitions of Intelligence 

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https://arxiv.org/abs/0706.3639

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A Collection of Definitions of Intelligence

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This paper is a survey of a large number of informal definitions of ``intelligence'' that the authors have collected over the years. Naturally, compiling a complete list would be impossible as many definitions of intelligence are buried deep inside articles and books.

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https://www.forskning.se/ai

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Här följer fler filmer och några artiklar som forskning.se tagit fram på temat artificiell intelligens.

Varför har vi ett medvetande?

Medvetande kan beskrivas som upplevelse. Men är det sammansatt av atomer? Eller helt skilt från det fysiska? Och varför har vi ett medvetande? Frågan har ställts i flera hundra år utan att vi fått något svar. I artikeln: Varför har vi ett medvetande? tas ett antal teorier upp om varför vi har ett medvetande.

Att förstå medvetandet kräver ett nytt vokabulär

Alla medvetandeprocesser samverkar i hjärnans nervsystem - i växelverkan med omgivningen. Men vi vet lika lite om medvetandet idag som folk visste om universum innan Kopernikus visade att jorden snurrar runt solen. I artikeln: Att förstå medvetandet kräver ett nytt vokabulär intervjuas Hans Liljenström, professor i biometri, SLU.

Vad är det att vara?

Det är svårt att studera medvetandet eftersom det är en så subjektiv upplevelse att vara. Första person-perspektivet gör att vetenskapliga krav på objektivitet i undersökningen blir svåra att uppnå. I filmen, som är på engelska, intervjuas Paavo Pylkkänen, lektor teoretisk filosofi, Högskolan i Skövde.

AI har ingen vilja.

Vi vill gärna tillskriva prylar mänskliga egenskaper, men AI är egentligen bara en lista med kommandon och en databas med uppgifter. AI vill inget, tänker inte, planerar inget och har inga åsikter. Det är däremot mänskligt att tillskriva maskiner mänskliga egenskaper. I filmen intervjuas Karim Jebari, fil. dr. i filosofi, Institutet för framtidsstudier.

Vad är medvetandet?

Det är ingen som vet vad medvetandet är, men om vi tittar på vilken konkret nytta det har för oss, så hjälper det oss att fatta beslut, att vara flexibla och att koordinera våra beteenden. Dessutom kan vi anta att det är medvetandet som ger mening med livet. Filmen visar en intervju på engelska med Paavo Pylkkänen, lektor teoretisk filosofi, Högskolan i Skövde.

Kvantfysik och artificiellt medvetande

Vägen till artificiellt medvetande kanske går via kvantmekaniken. Filmen är en intervju på engelska med Paavo Pylkkänen, lektor teoretisk filosofi, Högskolan i Skövde.

Artikel 1 av 18

Artiklar i serien Artificiell intelligens

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AI - Vad är artificiell intelligens? - forskning.se

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Artificiell intelligens, AI, skapas för att lösa uppgifter som att köra bil, känna igen ansikten och översätta språk. Mänsklig intelligens hjälper oss att dra lärdom av erfarenheter och utveckla vårt tänkande. Kan en konstgjord intelligens klara det? Vad är egentligen AI? Detta är första delen av totalt åtta från forskning.se på temat artificiell intelligens.

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https://www.arte.tv/en/videos/079461-001-A/use-technology/

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Use Technology! - Streetphilosophy - Watch the full show | ARTE

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Does technological progress help or hinder us as individuals? Jonas finds out by meeting a cycling enthusiast and a jujitsu champion.

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Noessel, Chris (2017). Designing Agentive Technology: AI That Works for People

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Nelson, Harold G. & Stolterman, Erik (2012). The Design Way: Intentional Change in an Unpredictable World

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positionera arbetet, inriktning, Frayling 1993. Hertzum 2014 - project designs for student design projects.

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Inlägg 1 Delprov 1: Funderade kring ämne till Delprov 1 Smarta system. Efter lite eftersökning inom AI och Big Data så fastnade intresset på smarta lösningar för matförsörjning och därför föll intresset på att söka vidare om samhällsnyttiga AI lösningar.