Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.
Sign Up For Latest News
Most deep learning networks today rely on dense representations. This is in stark contrast to our brains which are extremely sparse.
Most deep learning networks today rely on dense representations. This is in stark contrast to our brains which are extremely sparse.
Disruptive new technologies are often heralded for their power to transform industries, increase efficiency, and improve lives. However, emerging technologies such as artificial intelligence and quantum computing don’t just disrupt industries: they disrupt the workforce.
Disruptive new technologies are often heralded for their power to transform industries, increase efficiency, and improve lives. However, emerging technologies such as artificial intelligence and quantum computing don’t just disrupt industries: they disrupt the workforce.
Rob Reich, Associate Director, HAI
Rob is professor of political science and, by courtesy, professor of philosophy and at the Graduate School of Education, at Stanford University. He is the director of the Center for Ethics in Society and faculty co-director of the Center on Philanthropy and Civil Society (publisher of the Stanford Social Innovation Review), both at Stanford University. He is also associate director of the Institute on Human-Centered Artificial Intelligence.
He is the author or editor of several books on education and a book on the relationship between philanthropy, democracy, and justice: Just Giving: Why Philanthropy is Failing Democracy and How It Can Do Better (Princeton University Press 2018) and Philanthropy in Democratic Societies (edited with Chiara Cordelli and Lucy Bernholz). His current work focuses on ethics and technology, and he is editing a new volume called Digital Technology and Democracy (with Lucy Bernholz and Helene Landemore). He is the recipient of multiple teaching awards, including the Phi Beta Kappa Undergraduate Teaching Award and the Walter J. Gores Award, Stanford University. He is currently a University Fellow in Undergraduate Education at Stanford. He is a board member of the Spencer Foundation and the magazine Boston Review. Kate Vredenburgh, HAI-EIS Fellow Kate received Ph.D. in philosophy from Harvard University. She works mainly on questions in the philosophy of social science and political philosophy. The overarching motivation guiding her research is to understand how background commitments influence modeling in the social sciences and computer science, to reflect on how they should, and to build fairer models on that basis. She also works on political and ethical questions inspired by the use of technology and social science by corporations and by governments. For example, Kate is currently working on a project arguing for a right to explanation, inspired by recent discussions surrounding the EU's General Data Protection Regulation (GDPR) and interpretability in computer science. Kate will join the Center for Ethics as an interdisciplinary ethics fellow in partnership with the Stanford Institute for Human-Centered Artificial Intelligence. Todd Karhu, HAI-EIS Fellow Todd received his Ph.D. in philosophy from the London School of Economics. Before LSE, he completed an M.Phil. in political theory at Oxford University. His doctoral dissertation focuses on theoretical and practical issues in the ethics of killing, and a few other normative matters involving death. On the theoretical side, he has worked on the relationship between the wrongness of killing and the badness of death and about how killing and dying relate to the metaphysics of time. On the more practical side, he has worked on the question of the extent of one's right to self-defense in the context of war and the moral duties people incur in virtue of killing others.Rob Reich, Associate Director, HAI
Rob is professor of political science and, by courtesy, professor of philosophy and at the Graduate School of Education, at Stanford University. He is the director of the Center for Ethics in Society and faculty co-director of the Center on Philanthropy and Civil Society (publisher of the Stanford Social Innovation Review), both at Stanford University. He is also associate director of the Institute on Human-Centered Artificial Intelligence.
He is the author or editor of several books on education and a book on the relationship between philanthropy, democracy, and justice: Just Giving: Why Philanthropy is Failing Democracy and How It Can Do Better (Princeton University Press 2018) and Philanthropy in Democratic Societies (edited with Chiara Cordelli and Lucy Bernholz). His current work focuses on ethics and technology, and he is editing a new volume called Digital Technology and Democracy (with Lucy Bernholz and Helene Landemore). He is the recipient of multiple teaching awards, including the Phi Beta Kappa Undergraduate Teaching Award and the Walter J. Gores Award, Stanford University. He is currently a University Fellow in Undergraduate Education at Stanford. He is a board member of the Spencer Foundation and the magazine Boston Review. Kate Vredenburgh, HAI-EIS Fellow Kate received Ph.D. in philosophy from Harvard University. She works mainly on questions in the philosophy of social science and political philosophy. The overarching motivation guiding her research is to understand how background commitments influence modeling in the social sciences and computer science, to reflect on how they should, and to build fairer models on that basis. She also works on political and ethical questions inspired by the use of technology and social science by corporations and by governments. For example, Kate is currently working on a project arguing for a right to explanation, inspired by recent discussions surrounding the EU's General Data Protection Regulation (GDPR) and interpretability in computer science. Kate will join the Center for Ethics as an interdisciplinary ethics fellow in partnership with the Stanford Institute for Human-Centered Artificial Intelligence. Todd Karhu, HAI-EIS Fellow Todd received his Ph.D. in philosophy from the London School of Economics. Before LSE, he completed an M.Phil. in political theory at Oxford University. His doctoral dissertation focuses on theoretical and practical issues in the ethics of killing, and a few other normative matters involving death. On the theoretical side, he has worked on the relationship between the wrongness of killing and the badness of death and about how killing and dying relate to the metaphysics of time. On the more practical side, he has worked on the question of the extent of one's right to self-defense in the context of war and the moral duties people incur in virtue of killing others.
This workshop focused on “Uncertainty in AI Situations” asks researchers to consider what
an AI can do when faced with uncertainty. Machine learning algorithms whose
classifications rely on posterior probabilities of membership often present ambiguous
results, where due to unavailable training data or ambiguous cases, the likelihood of any
outcome is approximately even. In such situations, the human programmers must decide
how the machine handles ambiguity: whether making a “best-fit” classification or reporting
potential error, there is always a potential conflict between the mathematical rigor of the
model and the ambiguity of real-world use cases.
Some questions asked that begin the process of advancing AI to a new intellectual understanding of the trickiest problems in the machine-learning environment.
• How do researchers create training sets that engage with uncertainty, particularly
when deciding between reflecting real-world data and curating data sets to avoid
bias?
• How can we frame ontologies, typologies, and epistemologies that can account for,
and help solve, ambiguity in data and indecision in AI?
This workshop focused on “Uncertainty in AI Situations” asks researchers to consider what
an AI can do when faced with uncertainty. Machine learning algorithms whose
classifications rely on posterior probabilities of membership often present ambiguous
results, where due to unavailable training data or ambiguous cases, the likelihood of any
outcome is approximately even. In such situations, the human programmers must decide
how the machine handles ambiguity: whether making a “best-fit” classification or reporting
potential error, there is always a potential conflict between the mathematical rigor of the
model and the ambiguity of real-world use cases.
Some questions asked that begin the process of advancing AI to a new intellectual understanding of the trickiest problems in the machine-learning environment.
• How do researchers create training sets that engage with uncertainty, particularly
when deciding between reflecting real-world data and curating data sets to avoid
bias?
• How can we frame ontologies, typologies, and epistemologies that can account for,
and help solve, ambiguity in data and indecision in AI?
Van Ton-Quinlivan is a nationally recognized thought leader in workforce development, quoted in The New York Times, Chronicle of Higher Education, Stanford Social Innovation Review, U.S. News & World Report, and other publications.
Van Ton-Quinlivan is a nationally recognized thought leader in workforce development, quoted in The New York Times, Chronicle of Higher Education, Stanford Social Innovation Review, U.S. News & World Report, and other publications.
Twin revolutions at the start of the 21st century are shaking up the very idea of what it means to be human. Computer vision and image recognition are at the heart of the AI revolution. And CRISPR is a powerful new technique for genetic editing that allows humans to intervene in evolution.
Twin revolutions at the start of the 21st century are shaking up the very idea of what it means to be human. Computer vision and image recognition are at the heart of the AI revolution. And CRISPR is a powerful new technique for genetic editing that allows humans to intervene in evolution.
Good news! The near future has arrived and you’re ready to purchase your first fully autonomous vehicle. You have narrowed down your search to a few manufacturers and have just one decision left to make: How would you like your vehicle to respond if it finds itself in a potential collision with other autonomous vehicles?
Good news! The near future has arrived and you’re ready to purchase your first fully autonomous vehicle. You have narrowed down your search to a few manufacturers and have just one decision left to make: How would you like your vehicle to respond if it finds itself in a potential collision with other autonomous vehicles?
We will describe the Stanford Medicine Program for AI in Healthcare, which aims to bring AI into clinical use, safely and ethically. The session will begin with an overview of the effort and then focus on describing a project to improve palliative care using machine learning. We will summarize the creation and validation of a mortality prediction model, describe the associated care planning workflow it triggers and the work constraints it needs to function under. We will present preliminary results on an HAI supported project for understanding and addressing ethical challenges with implementation of machine learning to advance palliative care. Using this real-life example, we will elucidate several of the ethical challenges that need to be studied and addressed when combining artificial intelligence technologies with medical expertise to help doctors make faster, more informed and humane decisions.
We will describe the Stanford Medicine Program for AI in Healthcare, which aims to bring AI into clinical use, safely and ethically. The session will begin with an overview of the effort and then focus on describing a project to improve palliative care using machine learning. We will summarize the creation and validation of a mortality prediction model, describe the associated care planning workflow it triggers and the work constraints it needs to function under. We will present preliminary results on an HAI supported project for understanding and addressing ethical challenges with implementation of machine learning to advance palliative care. Using this real-life example, we will elucidate several of the ethical challenges that need to be studied and addressed when combining artificial intelligence technologies with medical expertise to help doctors make faster, more informed and humane decisions.
In this talk, Pamela Chen, 2020 Human-Centered AI and JSK Journalism Fellow at Stanford, shares her experiences leading an editorial team at Instagram as the company scaled content discovery to serve more than 1 billion monthly active users. Spoiler alert: it doesn’t go as planned.
In this talk, Pamela Chen, 2020 Human-Centered AI and JSK Journalism Fellow at Stanford, shares her experiences leading an editorial team at Instagram as the company scaled content discovery to serve more than 1 billion monthly active users. Spoiler alert: it doesn’t go as planned.
This conference is anchored and building on the release of the Special National Academy of Medicine (NAM) publication titled: “Artificial Intelligence in Healthcare: The Hope, The Hype, The Promise, The Peril.” Co-led by Michael Matheny and Sonoo Thadaney Israni.
Objectives At the conclusion of this activity, participants should be able to:Evaluate AI in the healthcare landscape
Critically assess the opportunities for AI in healthcare
Develop appropriate criteria for evaluating/deploying AI solutions
Build frameworks for creating and testing AI healthcare solutions
This conference is anchored and building on the release of the Special National Academy of Medicine (NAM) publication titled: “Artificial Intelligence in Healthcare: The Hope, The Hype, The Promise, The Peril.” Co-led by Michael Matheny and Sonoo Thadaney Israni.
Objectives At the conclusion of this activity, participants should be able to:Evaluate AI in the healthcare landscape
Critically assess the opportunities for AI in healthcare
Develop appropriate criteria for evaluating/deploying AI solutions
Build frameworks for creating and testing AI healthcare solutions
The next generation of user experiences will produce 1000-fold improvements in human capabilities. This new tools will amplify, augment, enhance, and empower people, just as the Web, email, search, navigation, digital photography, and many other applications have already done. Rather than emphasize autonomous machines and humanoid robots as team partners, these new tools will produce comprehensible, predictable, and controllable applications that promote self-efficacy, human responsibility, and social participation at scale. The goal is to ensure human control, while increasing the level of automation.
The next generation of user experiences will produce 1000-fold improvements in human capabilities. This new tools will amplify, augment, enhance, and empower people, just as the Web, email, search, navigation, digital photography, and many other applications have already done. Rather than emphasize autonomous machines and humanoid robots as team partners, these new tools will produce comprehensible, predictable, and controllable applications that promote self-efficacy, human responsibility, and social participation at scale. The goal is to ensure human control, while increasing the level of automation.
Conversations about ethics and AI are commonplace today, but they are often pitched at a high level of generality or abstraction. In this workshop, we gathered together leading young scholars, chiefly philosophers, to discuss a more detailed research agenda with a particular focus on moral and political philosophy and their intersections with AI. Topics included AI and explainability, AI and value alignment, governance of AI, and more.
Conversations about ethics and AI are commonplace today, but they are often pitched at a high level of generality or abstraction. In this workshop, we gathered together leading young scholars, chiefly philosophers, to discuss a more detailed research agenda with a particular focus on moral and political philosophy and their intersections with AI. Topics included AI and explainability, AI and value alignment, governance of AI, and more.
China’s government and tech industry have great ambitions for artificial intelligence development and leadership, and Chinese society is facing economic, ethical, and regulatory challenges related to AI much like those around the world. At a time when the U.S. and Chinese governments are locked in escalating disputes over technology and trade, understanding Chinese ambitions, realities, and politics surrounding digital technologies is ever more important. Hosted by DigiChina, a project of the Stanford Cyber Policy Center Program on Geopolitics, Technology and Governance and the New America Cybersecurity Initiative. Speakers include: Shazeda Ahmed, PhD Candidate, UC Berkeley School of Information; Predoctoral Fellow, Stanford Institute for Human-Centered Artificial Intelligence and Center for International Security and CooperationRogier Creemers, Assistant Professor in the Law and Governance of China, University of LeidenAndrew Grotto, William J. Perry International Security Fellow and Director of the Program on Geopolitics, Technology and Governance at the Stanford Cyber Policy Center; Visiting Fellow, Hoover InstitutionSamm Sacks, Cybersecurity Policy and China Digital Economy Fellow, New AmericaKatharin Tai, PhD Student, Political Science, Massachusetts Institute of TechnologyGraham Webster, Coordinating Editor, Stanford-New America DigiChina Project; China Digital Economy Fellow, New AmericaWu Shenkuo, Professor of Law, Beijing Normal UniversityJulia Voo, Research Director, China Cyber Policy Initiative, Harvard Belfer CenterYuan Yang, China Technology Correspondent, Financial Times The organizers are grateful for the support of the Harvard-MIT Ethics and Governance of AI Initiative. Please register at https://www.eventbrite.com/e/ai-and-digital-policy-in-china-tickets-76168400737 *Please park in the Galvez Lot (L-96) in one of the spaces with a HAI Only reserved sign.
China’s government and tech industry have great ambitions for artificial intelligence development and leadership, and Chinese society is facing economic, ethical, and regulatory challenges related to AI much like those around the world. At a time when the U.S. and Chinese governments are locked in escalating disputes over technology and trade, understanding Chinese ambitions, realities, and politics surrounding digital technologies is ever more important. Hosted by DigiChina, a project of the Stanford Cyber Policy Center Program on Geopolitics, Technology and Governance and the New America Cybersecurity Initiative. Speakers include: Shazeda Ahmed, PhD Candidate, UC Berkeley School of Information; Predoctoral Fellow, Stanford Institute for Human-Centered Artificial Intelligence and Center for International Security and CooperationRogier Creemers, Assistant Professor in the Law and Governance of China, University of LeidenAndrew Grotto, William J. Perry International Security Fellow and Director of the Program on Geopolitics, Technology and Governance at the Stanford Cyber Policy Center; Visiting Fellow, Hoover InstitutionSamm Sacks, Cybersecurity Policy and China Digital Economy Fellow, New AmericaKatharin Tai, PhD Student, Political Science, Massachusetts Institute of TechnologyGraham Webster, Coordinating Editor, Stanford-New America DigiChina Project; China Digital Economy Fellow, New AmericaWu Shenkuo, Professor of Law, Beijing Normal UniversityJulia Voo, Research Director, China Cyber Policy Initiative, Harvard Belfer CenterYuan Yang, China Technology Correspondent, Financial Times The organizers are grateful for the support of the Harvard-MIT Ethics and Governance of AI Initiative. Please register at https://www.eventbrite.com/e/ai-and-digital-policy-in-china-tickets-76168400737 *Please park in the Galvez Lot (L-96) in one of the spaces with a HAI Only reserved sign.
HAI's October 28-29 conference on AI Ethics, Policy, and Governance at Stanford University will convene experts and leaders from academia, industry, civil society, and government to explore critical and emerging issues related to understanding and guiding AI's human and societal impact. Through plenary discussions, breakout sessions, and workshops we will explore the latest research, delve into case studies, illuminate best practices, and build a global community of research, policy, and practice committed to ensuring that AI benefits humanity.
HAI's October 28-29 conference on AI Ethics, Policy, and Governance at Stanford University will convene experts and leaders from academia, industry, civil society, and government to explore critical and emerging issues related to understanding and guiding AI's human and societal impact. Through plenary discussions, breakout sessions, and workshops we will explore the latest research, delve into case studies, illuminate best practices, and build a global community of research, policy, and practice committed to ensuring that AI benefits humanity.
Jens Hainmueller is a Professor in the Department of Political Science at Stanford University and holds a courtesy appointment in the Stanford Graduate School of Business. He is also the Faculty Co-Director of the Stanford Immigration Policy Lab that is focused on the design and evaluation of immigration and integration policies and programs.
Jens Hainmueller is a Professor in the Department of Political Science at Stanford University and holds a courtesy appointment in the Stanford Graduate School of Business. He is also the Faculty Co-Director of the Stanford Immigration Policy Lab that is focused on the design and evaluation of immigration and integration policies and programs.
Abstract Submission & Review
The review process will be coordinated by the editorial team of Nature Medicine. Reviewers will rate abstracts based on scientific merit and potential for impact on healthcare value at scale within 10 years, especially for medically fragile and costly population segments. Examples of fragile and costly patients are those receiving inpatient care, frail seniors seeking to maintain independence at home, or children with chronic illnesses or social health risks. Submitted abstracts should describe the topical background, methods, results and implications for improving the value of care, and indicate the category in which these should be considered among the six described above. Abstracts can be considered for oral presentations in a maximum of 2 categories. All authors whose abstracts exceed a threshold score to be determined after review may opt to have their abstract published, via an online appendix, to a report on conference proceedings. This will not be indexed in PubMed but will be available online. Publication of conference proceedings papers in this format do not generally preclude consideration of the full manuscript in other scientific journals, provided the submission provides a substantive extension of results, methodology, application, analysis, conclusions and/or implications over the conference proceedings paper. If figures or any other part of the paper is reproduced from the conference proceedings article, authors must be responsible for securing any necessary rights. The Nature Research policy can be found here: https://www.nature.com/authors/policies/preprints.html The 2nd and 3rd place winners in each category will have the opportunity to participate in the session topic Q&A as well as present a poster during the conference. Submission Details Deadline: April 30, 2019 The maximum abstract length is two pages (excluding references). Figures and images may be included in the abstract. All submissions should be in 11-point Times New Roman font with 1” margins on all sides. Because reviewers will be blinded to the author’s identities, do not include the names of authors, institutions, or any other identifying information in the initial submission. Research that has been previously published elsewhere or is currently in submission may be submitted. Please direct questions about abstract submission to Javier.Carmona@us.nature.com and about the conference to pac-conference@stanford.edu. To submit an abstract please email to javier.carmona@us.nature.com with the subject line “FAC Abstract Submission.”Registration Information
| Early (by 5/1) | Regular (by 9/18) | |
| Industry | $700 | $950 |
| Academics | $350 | $500 |
| Students | $75 | $85 |
Abstract Submission & Review
The review process will be coordinated by the editorial team of Nature Medicine. Reviewers will rate abstracts based on scientific merit and potential for impact on healthcare value at scale within 10 years, especially for medically fragile and costly population segments. Examples of fragile and costly patients are those receiving inpatient care, frail seniors seeking to maintain independence at home, or children with chronic illnesses or social health risks. Submitted abstracts should describe the topical background, methods, results and implications for improving the value of care, and indicate the category in which these should be considered among the six described above. Abstracts can be considered for oral presentations in a maximum of 2 categories. All authors whose abstracts exceed a threshold score to be determined after review may opt to have their abstract published, via an online appendix, to a report on conference proceedings. This will not be indexed in PubMed but will be available online. Publication of conference proceedings papers in this format do not generally preclude consideration of the full manuscript in other scientific journals, provided the submission provides a substantive extension of results, methodology, application, analysis, conclusions and/or implications over the conference proceedings paper. If figures or any other part of the paper is reproduced from the conference proceedings article, authors must be responsible for securing any necessary rights. The Nature Research policy can be found here: https://www.nature.com/authors/policies/preprints.html The 2nd and 3rd place winners in each category will have the opportunity to participate in the session topic Q&A as well as present a poster during the conference. Submission Details Deadline: April 30, 2019 The maximum abstract length is two pages (excluding references). Figures and images may be included in the abstract. All submissions should be in 11-point Times New Roman font with 1” margins on all sides. Because reviewers will be blinded to the author’s identities, do not include the names of authors, institutions, or any other identifying information in the initial submission. Research that has been previously published elsewhere or is currently in submission may be submitted. Please direct questions about abstract submission to Javier.Carmona@us.nature.com and about the conference to pac-conference@stanford.edu. To submit an abstract please email to javier.carmona@us.nature.com with the subject line “FAC Abstract Submission.”Registration Information
| Early (by 5/1) | Regular (by 9/18) | |
| Industry | $700 | $950 |
| Academics | $350 | $500 |
| Students | $75 | $85 |
Does AI belong in the classroom? Will tomorrow’s classroom look like today’s smart home? Is AI in the classroom a boon or a curse? How can educators and technologists work together to develop tools and methods that facilitate the learning experience? Can intelligent learning promote personalized intellectual exploration? This Intersections event puts faculty from the Stanford Graduate School of Education and the Stanford School of Engineering in conversation.
Does AI belong in the classroom? Will tomorrow’s classroom look like today’s smart home? Is AI in the classroom a boon or a curse? How can educators and technologists work together to develop tools and methods that facilitate the learning experience? Can intelligent learning promote personalized intellectual exploration? This Intersections event puts faculty from the Stanford Graduate School of Education and the Stanford School of Engineering in conversation.