Keynote Speakers

Rediet Abebe is a Junior Fellow at the Harvard Society of Fellows and an incoming Assistant Professor of Computer Science at the University of California, Berkeley. Abebe holds a Ph.D. in computer science from Cornell University as well as graduate degrees from Harvard University and the University of Cambridge. Her research is in the fields of artificial intelligence and algorithms, with a focus on equity and justice concerns. She co-founded and co-organizes Mechanism Design for Social Good (MD4SG), a multi-institutional, interdisciplinary research initiative working to improve access to opportunity for historically disadvantaged communities. Abebe's research has informed policy and practice at the National Institute of Health (NIH) and the Ethiopian Ministry of Education. Abebe has been honored in the MIT Technology Reviews' 35 Innovators Under 35, ELLE, and the Bloomberg 50 list as a "one to watch." She has presented her research in venues including National Academy of Sciences, the United Nations, and the Museum of Modern Art. Abebe co-founded Black in AI, a non-profit organization tackling representation and inclusion issues in AI. Her research is deeply influenced by her upbringing in her hometown of Addis Ababa, Ethiopia.

Solon Barocas is a Principal Researcher in the New York City lab of Microsoft Research, Assistant Professor in the Department of Information Science at Cornell, and Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard. His current research explores ethical and policy issues in artificial intelligence, particularly fairness in machine learning, methods for bringing accountability to automated decision-making, and the privacy implications of inference. Solon co-founded the annual workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) and later established the ACM conference on Fairness, Accountability, and Transparency (FAccT).

Alice Xiang is the Head of Fairness, Transparency, and Accountability Research at the Partnership on AI, where she leads and manages a team of interdisciplinary researchers. Core areas of her research include bridging technical and legal approaches to algorithmic bias, assessing explainability techniques in deployment, and examining risk assessment tools. Alice’s work sits at the intersection of social justice and AI; she seeks to tackle the ways in which algorithmic decision-making can further entrench societal biases and inequalities. She has been quoted in Axios, Mercury News, and VentureBeat, among others, for her work on algorithmic bias and transparency, criminal justice risk assessment tools, and the limitations of AI. Prior to joining PAI, Alice worked as an attorney at Gunderson Dettmer, representing startups and venture capital firms. She has also worked in civil appellate litigation at the Department of Justice, econometrics research at the Federal Reserve, and data science at LinkedIn. Alice holds a Juris Doctor from Yale Law School, a Master’s in Development Economics from Oxford, a Master’s in Statistics from Harvard, and a Bachelor’s in Economics from Harvard.

Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair and has joint appointments in the Wharton School.He is founder of Penn’s Networked and Social Systems Engineering (NETS) program, and director of Penn’s Warren Center for Network and Data Sciences. His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. He has worked and consulted extensively in the technology and finance industries. He is a fellow of the American Academy of Arts and Sciences, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence. Kearns has consulted widely in the finance and technology industries, including a current role as an Amazon Scholar.

Berk Ustun is a Postdoctoral Fellow at the Harvard Center for Research on Computation and Society. He will be starting as a Visiting Faculty Researcher at Google AI in 2020, and as an Assistant Professor in the Halicioglu Data Science Institute at UC San Diego in 2021. Berk's research interests are in machine learning, optimization, and human-centered design. He develops methods to promote the adoption and responsible use of machine learning in domains such as medicine, consumer finance, and criminal justice. His work has been covered by media outlets, including NPR and Wired, and has won major awards, including the INFORMS Innovative Applications in Analytics Award in 2016 and 2019, and the INFORMS Computing Society Best Student Paper.Berk holds a PhD in Electrical Engineering and Computer Science from MIT, an MS in Computation for Design and Optimization from MIT, and BS degrees in Operations Research and Economics from UC Berkeley.

Nikita Aggarwal is a Research Associate in the Oxford Internet Institute's Digital Ethics Lab, as well as a doctoral candidate at the Faculty of Law, University of Oxford. Her research examines the legal and ethical challenges due to emerging, data-driven technologies, with a particular focus on machine learning in consumer lending. She also researches blockchain governance, and internet platform regulation more generally. Prior to entering academia, Nikita was an attorney in the legal department of the International Monetary Fund, where she advised on financial sector law reform in the Euro area and worked extensively on initiatives to reform legal and policy frameworks for sovereign debt restructuring. She previously practiced as an associate with Clifford Chance LLP, where she specialized in EU financial regulation and sovereign debt restructuring. She earned her law degree (LLB) from the London School of Economics and Political Science, and is a solicitor of England and Wales.

Jiahao Chen is a Senior Vice President and Research Lead at JPMorgan AI Research in New York, with research focusing on explainability and fairness in machine learning, as well as semantic knowledge management. He was previously a Senior Manager of Data Science at Capital One focusing on machine learning research for credit analytics and retail operations. When still in academia, Jiahao was a Research Scientist at MIT CSAIL where he co-founded and led the Julia Lab, focusing on applications of the Julia programming language to data science, scientific computing, and machine learning. Jiahao has organized JuliaCon, the Julia conference, for the years 2014-2016, as well as organized workshops at NeurIPS, SIAM CSE, and the American Chemical Society National Meetings. Jiahao has authored over 120 packages for numerical computation, data science and machine learning for the Julia programming language, in addition to numerous contributions to the base language itself.

Madeleine Clare Elish is a cultural anthropologist whose work examines the social impacts of AI and automation on society. She recently joined Google as a Senior Research Scientist working on the Ethical AI team. Previously, she co-founded and led the AI on the Ground Initiative at Data & Society Research Institute, which uses social science research to inform future design, use, and governance of automated and AI systems. She has conducted field work across varied industries and communities, ranging from the Air Force, the driverless car industry, and commercial aviation to precision agriculture and emergency healthcare. Her research has been published and cited in scholarly journals as well as publications including The New York Times, Wired, The Guardian, MIT Tech Review, Vice, and USA Today. She holds a PhD in Anthropology from Columbia University and an S.M. in Comparative Media Studies from MIT.

Jonathan Bryant is Director of Technology at Financial Industry Regulatory Authority (FINRA) where he assists in the adoption of Machine Learning across the organization. His interests focus on enabling enterprises to develop robust impact-driven practices in data science and machine learning. Dr. Bryant received formal academic training in Medical Physics at the University of Chicago and Harvard Medical School, focused on imaging in Radiation Oncology, applied mathematics and inverse problems. Recognizing the growing role private enterprises play in the development of machine learning, Jonathan pursued roles in management consulting, corporate strategy and technology, building a holistic view of how organizations can succeed in leveraging their data with adoption of increasingly sophisticated quantitative techniques. He hypothesizes that new partnership models must emerge between the academic, technology and business communities to realize the transformational change that machine learning promises.