Rediet Abebe is an Assistant Professor of Computer Science at the University of California, Berkeley and a Junior Fellow at the Harvard Society of Fellows. Abebe holds a Ph.D. in computer science from Cornell University and graduate degrees in mathematics from Harvard University and the University of Cambridge. Her research is in artificial intelligence and algorithms, with a focus on equity and justice concerns. Abebe co-founded and co-organizes Mechanism Design for Social Good (MD4SG) -- a multi-institutional, interdisciplinary initiative. Her dissertation received the 2020 ACM SIGKDD Dissertation Award and an honorable mention for the ACM SIGEcom Dissertation Award for offering the foundations of this emerging research area. Abebe's work has informed policy and practice at the National Institute of Health (NIH) and the Ethiopian Ministry of Education. She has been honored in the MIT Technology Reviews' 35 Innovators Under 35 and the Bloomberg 50 list as a one to watch. Abebe also co-founded Black in AI, a non-profit organization tackling equity issues in AI. Her research is influenced by her upbringing in her hometown of Addis Ababa, Ethiopia.

Elisa Celis is an assistant professor in the Statistics & Data Science department at Yale University. She studies the manifestation of social and economic biases in our online lives via the algorithms that encode and perpetuate them. Her research leverages both experimental and theoretical approaches, and her work spans multiple disciplines including data science, machine learning, fairness in socio-technical systems and algorithm design.

Sanmay Das is a Professor of Computer Science at George Mason University. His research interests are in designing effective algorithms for agents in complex, uncertain environments, and in understanding the social or collective outcomes of individual behavior. Dr. Das is chair of the ACM Special Interest Group on Artificial Intelligence, a member of the board of directors of the International Foundation for Autonomous Agents and Multiagent Systems, and serves as an associate editor of the ACM Transactions on Economics and Computation and of the Journal of Artificial Intelligence Research. Dr. Das has served as program co-chair of the AAMAS and AMMA conferences, in addition to regularly serving as an area chair or senior program committee member of major conferences including IJCAI, AAAI, EC, and AAMAS. He has been recognized with awards for research and teaching, including an NSF CAREER Award and the Department Chair Award for Outstanding Teaching at Washington University.

Maria De-Arteaga is an Assistant Professor at the Information, Risk and Operations Management Department at the University of Texas at Austin. She received a joint doctorate in machine learning and public policy from Carnegie Mellon University. Her research focuses on the risks and opportunities of using machine learning for decision support in high-stakes settings. Her work has been awarded the Best Thematic Paper Award at NAACL’19, the Innovation Award on Data Science at Data for Policy’16 and has been featured by UN Women and Global Pulse in their report “Gender Equality and Big Data: Making Gender Data Visible.” She is a recipient of a 2020 Google Award for Inclusion Research, a 2018 Microsoft Research Dissertation Grant, and was named an EECS 2019 Rising Star. In 2017, she co-founded the Machine Learning for the Developing World (ML4D) Workshop series at NeurIPS.

Sina Fazelpour is a Social Sciences and Humanities Research Council Postdoctoral Fellow in the Department of Philosophy at Carnegie Mellon University, with a secondary affiliation with the Machine Learning Department. He is also the Council Fellow on the World Economic Forum's Global Future Council on Data Policy. In Summer 2021, he will be joining Northeastern University as an Assistant Professor of Philosophy and Computer Science in the Department of Philosophy and Religion in the College of Social Sciences and Humanities and in the Khoury College of Computer Sciences. He holds a PhD in Philosophy from the University of British Columbia, a M.Sc in medical biophysics from the University of Toronto, and a B.Eng in electrical and biomedical engineering from McMaster University. His primary research focus is on issues of justice, reliability, and accountability in predictive and decision-making algorithms. He also works on understanding the concepts and consequences of diversity concepts and consequences of diversity in social groups and networks. He has published in the philosophy of science, cognitive science and ethics of artificial intelligence, and his research has been supported by Joseph-Armand Bombardier Canada Graduate Scholarship, the Block Center for Technology and Society, the Templeton Foundation, and Natural Sciences and Engineering Research Council of Canada.

Patrick J. Fowler's research aims to prevent homelessness and its deleterious effects on child, family, and community well-being. Trained in child clinical-community psychology, Fowler uses innovative methods that rigorously investigate policies and programs intended to promote housing and family stability. Recent research focuses on cross system collaborations to prevent child maltreatment associated with family homelessness, as well as youth homelessness in the transition from foster care to adulthood.

Dr. Chyna Hill is a Postdoctoral Researcher with the Center for Artificial Intelligence in Society and. Dr. Hill is the community-Based Research Lead on the Coordinated Entry System Triage Tool Research & Refinement Project. Recently, she obtained her MSW and PhD from the Suzanne Dworak-Peck School of Social Work at the University of Southern California. Dr. Hill’s work explores the complex nature of Black homelessness. Her research explicitly elucidates how historical inequities and byproducts of discrimination hinder Black people from obtaining and maintaining stable housing. Using machine learning techniques, Dr. Hill combines theories specific to marginalized populations (i.e., intersectionality, critical race theory, etc.) with machine learning analyses to explore predictors of housing instability, intervention assignments, and success in housing outcomes among Black people experiencing homelessness.

Daniel E. Ho is the William Benjamin Scott and Luna M. Scott Professor of Law at Stanford Law School, Professor of Political Science, and Senior Fellow at the Stanford Institute for Economic Policy Research. He is also Associate Director of the Stanford Institute for Human-Centered Artificial Intelligence, Faculty Fellow at the Center for Advanced Study in the Behavioral Sciences, and is Director of the Regulation, Evaluation, and Governance Lab (RegLab). He received his J.D. from Yale Law School and Ph.D. from Harvard University and clerked for Judge Stephen F. Williams on the U.S. Court of Appeals, District of Columbia Circuit. Ho previously served as president for the Society of Empirical Legal Studies and co-editor of the Journal of Law, Economics, & Organization.

Caroline Johnston is a 2nd year PhD student in Industrial & Systems Engineering at the University of Southern California (USC), advised by Dr. Phebe Vayanos. Caroline's research interests lie at the intersection of robust optimization and fairness/equity in AI. She is passionate about applying her technical skills to create positive, real-world social impact. Caroline is a recipient of the NSF GRFP Award and is a student representative for the Center for AI in Society (CAIS) at USC.

Dr. Ece Kamar is a Senior Principal Research Area Manager at Microsoft Research Redmond overseeing the research on Human-centered AI. Her research investigates research problems at the intersection of AI systems, people, and our society; exploring how limitations of AI systems lead to concerns around biases, reliability, and safety problems, investigating novel ways for AI systems to support people, and finally developing frameworks for human-AI teamwork for complementarity. Ece also serves as the Technical Advisor of Microsoft's company-wide committee on AI, Ethics and Effects in Engineering and Research (AETHER). In her advisory role, she consults product teams at Microsoft on issues around Responsible AI, helps to develop best practices, tools, and guidance to support the development of responsible, unbiased, and reliable AI systems for the open world.

Hima Lakkaraju is an Assistant Professor at Harvard University focusing on explainability, fairness, and robustness of machine learning models. She has also been working with various domain experts in criminal justice and healthcare to understand the real-world implications of explainable and fair ML. Hima has recently been named one of the 35 innovators under 35 by MIT Tech Review and has received best paper awards at SIAM International Conference on Data Mining (SDM) and INFORMS. She has given invited workshop talks at ICML, NeurIPS, AAAI, and CVPR, and her research has also been covered by various popular media outlets including the New York Times, MIT Tech Review, TIME, and Forbes. For more information, please visit: https://himalakkaraju.github.io/

Dr. Nicol Turner Lee is a senior fellow in Governance Studies, the director of the Center for Technology Innovation, and serves as Co-Editor-In-Chief of TechTank. Dr. Turner Lee researches public policy designed to enable equitable access to technology across the U.S. and to harness its power to create change in communities across the world. Her work also explores global and domestic broadband deployment and internet governance issues. She is an expert on the intersection of race, wealth, and technology within the context of civic engagement, criminal justice, and economic development.

Vinodkumar Prabhakaran is a Research Scientist in the Ethical AI team in Google Research. His research focuses on cross cultural considerations in AI fairness and ethics inquiries. Prior to Google, he was a postdoctoral researcher at Stanford University, and obtained his PhD in computer science from Columbia University. His prior research focused on building NLP/ML-aided scalable ways to identify and address large-scale societal issues such as racial disparities in policing, workplace incivility, and online abuse."

Eric Rice is an associate professor and the founding co-director of the USC Center for Artificial Intelligence in Society, a joint venture of the USC Suzanne Dworak-Peck School of Social Work and the USC Viterbi School of Engineering. Rice received a BA from the University of Chicago, and an MA and PhD in Sociology from Stanford University. He was a postdoctoral fellow at the University of California, Los Angeles. He specializes in social network science and theory, as well as community-based research. His primary focus is on youth experiencing homelessness and how issues of social network influence may affect risk-taking behaviors and resilience. For several years he has been working with computer scientists to merge social work science and AI, seeking novel solutions to major social problems such as homelessness and HIV. Rice is the author of more than 150 peer-reviewed articles. He is the recipient of grants from the National Institute of Mental Health, the California HIV/AIDS Research Program, the Army Research Office and other agencies. Since 2002 he has worked closely with homeless youth providers in Los Angeles and many other communities across the country. He is the creator of the TAY Triage Tool—to identify high-risk homeless youth for prioritizing them for supportive housing—which was incorporated into Orgcode’s Next Step Tool for homeless youth.

Maria Rodriguez

Dr. Rodriguez is an Assistant Professor at the School of Social Work, University at Buffalo (SUNY); a Faculty Associate at the BerkmanKlein Center for Internet and Society at Harvard University; a Faculty Fellow at the Center for Democracy and Technology; as well as a member of the Twitter Academic Research Advisory Board. Her work lies at the intersection of computational social science, demography, and social policy.

Francesca Rossi is an IBM fellow and the IBM AI Ethics Global Leader. She works at the IBM T.J. Watson Research Lab in New York, USA, where she leads research projects on Artificial Intelligence (AI), the research area that she has focused on during her entire career. She obtained her Ph.D. in Computer Science from the University of Pisa, where she remained for 6 years as an assistant professor. Afterwards, she joined the University of Padova, where she has been a professor of computer science for about 20 years before joining IBM. She published over 200 scientific articles. In 2019 she published the book for the Italian market “Il confine del futuro: ci possiamo fidare dell’Intelligenza Artificiale?” with Feltrinelli. She is a fellow of both the worldwide association of AI (AAAI) and of the European one (EurAI), and she will be the next president of AAAI. In 2020 she was the general chair of the AAAI 2020 conference, that saw the participation of more than 4,000 AI researchers. Besides her scientific results and her activities in support of the AI research community, in the last few years she has become a leader also in AI ethics, founding the IBM internal AI ethics board and leading or contributing to the main global initiatives around this topic. For example, she has been a member of the European Commission High Level Expert Group on AI and currently collaborates on these themes with many research centers in USA, UK, Europe, and Australia, with the United Nations, and with the World Economic Forum. She is also part of other high-impact initiatives, such as the Global Partnership on AI, a coalition that includes about 20 countries (including USA and Italy) with the aim to facilitate the international collaboration on the responsible development and use of AI.

Dr. Karen Smilowitz is the James N. and Margie M. Krebs Professor in Industrial Engineering and Management Science at Northwestern University, with a joint appointment in the Operations group at the Kellogg School of Management. Dr. Smilowitz is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and non-profit applications, working with transportation providers, logistics specialists and a range of non-profit organizations. Dr. Smilowitz is the founder of the Northwestern Initiative on Humanitarian and Non-Profit Logistics. She has been instrumental in promoting the use of operations research within the humanitarian and nonprofit sectors through the Woodrow Wilson International Center for Scholars, the American Association for the Advancement of Science, and the National Academy of Engineering, as well as various media outlets. Dr. Smilowitz is Editor-in-Chief of Transportation Science. Dr. Smilowitz received the Award for the Advancement of Women in OR/MS from INFORMS and led the winning team in the INFORMS Innovative Applications of Analytics Award.

Dr. Erika Van Buren serves as the Chief Innovation Officer for First Place for Youth, where she leads the evaluation, learning and national expansion strategy for scaling First Place’s influence and impact in service to older foster youth across the country. She crafts and implements the internal and external evaluation agenda for the agency, works closely with program leadership to innovate and roll-out best and evidence-supported strategies to improve practice, and conducts on-going sector building and systems change activities in support of First Place’s mission. Dr. Van Buren received her BA degree from Yale University, and a doctorate in clinical psychology from the University of California, Los Angeles. With over 20 years of experience, she has cultivated expertise in the areas of community mental health and child welfare program development and evaluation, quality improvement and performance management practices and was most recently named as a member of the 11th class of Annie E. Casey Foundation Leadership Fellows.

Phebe Vayanos is an Assistant Professor of Industrial & Systems Engineering and Computer Science at the University of Southern California, and Associate Director of the CAIS Center for Artificial Intelligence in Society. Prior to joining USC, she was a lecturer in the Operations Research and Statistics Group at the MIT Sloan School of Management, and a postdoctoral research associate in the Operations Research Center at MIT. She holds a PhD degree in Operations Research and an MEng degree in Electrical & Electronic Engineering, both from Imperial College London. Her research is focused on Artificial Intelligence and Operations Research and in particular on optimization, machine learning, and game theory. She aims to build foundational knowledge in these areas to enable the design of intelligent systems that can operate reliably in the open world, in complex, uncertain environments, and against strategic adversaries. She designs algorithms that are suitable for use by human decision-makers, that are transparent and interpretable, and that integrate human value judgments. Her research is motivated by problems that are important for social good and aims to craft solutions that are fair and non-discriminatory, and therefore suitable to be deployed in our society. Her aim is to advance research in Operations Research and Artificial Intelligence in a manner that will benefit society and in particular low resource communities and others that have not benefited from these recent developments.