Solon Barocas, Information Science

Solon Barocas is Assistant Professor in the Department of Information Science at Cornell University. He is also a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University. 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. He 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 (FAT*).

Relevant work includes Big Data’s Disparate ImpactFairness and Machine Learning, The Intuitive Appeal of Explainable Machines, and Engaging the Ethics of Data Science in Practice.

Jon Kleinberg, Computer Science and Information Science

Jon Kleinberg is the Tisch University Professor in the Departments of Computer Science and Information Science at Cornell University. His research focuses on the interaction of algorithms and networks, and the roles they play in large-scale social and information systems. He is a member of the National Academy of Sciences and the National Academy of Engineering and the recipient of research fellowships from the MacArthur, Packard, Simons, and Sloan Foundations, as well as the Harvey Prize, Nevanlinna Prize, and ACM Prize in Computing.

Relevant work includes Human Decisions and Machine Predictions and Inherent Trade-Offs in the Fair Determination of Risk Scores.

Karen Levy, Information Science

Karen Levy is an assistant professor in the Department of Information Science at Cornell University and associated faculty at Cornell Law School. She researches the social, legal, and ethical implications of new technologies, with particular focus on inequality, work, and intimacy. She holds a Ph.D. in Sociology from Princeton University and a J.D. from Indiana University Maurer School of Law. Karen’s research has been supported by the Sloan, Borchard, Horowitz, and National Science Foundations. She is currently at work on a book tracing the emergence of monitoring technologies in the United States long-haul trucking industries.

Relevant work includes Automation is Coming for Truckers. But First, They are being Watched and Privacy, Poverty, and Big Data: A Matrix of Vulnerabilities for Poor Americans.

Helen Nissenbaum, Information Science

Helen Nissenbaum is a professor in the Department of Information Science at Cornell Tech. Her research takes an ethical perspective on policy, law, science, and engineering relating to information technology, computing, digital media and data science. Topics have included privacy, trust, accountability, security, and values in technology design. She is the recipient of the 2014 Barwise Prize of the American Philosophical Association.

Relevant work includes Obfuscation: A User’s Guide for Privacy and Protest and Privacy in Context: Technology, Policy, and the Integrity of Social Life.


David Robinson, Information Science; Managing Director (on leave), Upturn

David Robinson studies the public interest oversight of automated judgment. His recent scholarship has focused on the governance of AI applications in criminal justice, including pretrial risk assessments and predictive policing tools. While at Cornell, he is exploring new avenues of research related to successful oversight mechanisms for AI. David is on leave from his role as a Managing Director at Upturn.

Relevant Work includes The Challenges of Prediction: Lessons from Criminal Justice, Danger Ahead: Risk Assessment and the Future of Bail Reform, and Pretrial Risk Assessments: A Practical Guide for Judges.


Hoda Heidari, Computer Science

Hoda Heidari is a Post-Doctoral Associate at the department of Computer Science at Cornell. She previously spent two years at ETH Zürich, Institute for Machine Learning, working with Professor Andreas Krause. During that time, she also collaborated with Professor Krishna Gummadi. Hoda received her Ph.D. in Computer and Information Science from the University of Pennsylvania, where she was co-advised by Professors Michael Kearns and Ali Jadbabaie. She is broadly interested in Societal Aspects of Artificial Intelligence and Machine Learning and Algorithmic Economics.

Sarah Sachs, Science & Technology Studies

Sarah Sachs is a postdoctoral researcher in the Department of Science & Technology Studies, working with Stephen Hilgartner and Malte Ziewitz on developing the Data Science & Society Lab. Sarah received her PhD in Sociology from Columbia University, with a focus on work; organizations; and the sociology of science, knowledge, and technology. In her dissertation project, The Algorithm at Work: The Reconfiguration of Work and Expertise in the Making of Similarity in Art Data, Sarah studied a team of art experts as they classified and annotated art data within a private sector context. Working with an art similarity matching algorithm, much of their work demanded the ability to predict how the algorithm and the data would work on one another in practice. Sarah examined the distributed process through which the team explained and repaired breakdowns between the output and their expectations, from which new forms of knowledge emerged. Sarah is currently considering how the framework of distributed explanation and repair may be useful for analyzing computer science work, in particular the everyday decision-making practices involved in the development and refinement of machine learning algorithms.

Relevant work includes The algorithm at work? Explanation and repair in the enactment of similarity in art data.

A. Feder Cooper, Computer Science

Cooper is a PhD student in the Department of Computer Science at Cornell University, advised by Chris De Sa. Cooper’s research broadly focuses on the intersection between distributed systems and machine learning, and more specifically on designing techniques for building scalable, efficient, and provably correct machine learning systems. Cooper also investigates the legal and social dimensions of this work, collaborating with professors in both the Department of Information Science and Law.

Fernando Delgado, Information Science

Fernando Delgado is a PhD student in the Department of Information Science at Cornell University. His research focuses on the adoption of AI technologies in the practice of law and justice system more broadly. Prior to arriving at Cornell, he worked in industry at H5 Technologies designing and evaluating information retrieval and machine learning systems deployed in civil litigation discovery, antitrust compliance, and white-collar crime investigations.

Relevant work includes IEEE’s Global Initiative on Ethics of Autonomous and Intelligent Systems (drafting member for Law Committee) and The Role of Metadata in Machine Learning for Technology Assisted Review.

Kate Donahue, Computer Science

Kate Donahue is a PhD student in the Department of Computer Science at Cornell University. She is interested in machine learning, theory, and applying those two areas to make machine learning more robust and fair in real-life applications. Previously, she worked as a data scientist at Booz Allen Hamilton working on applying machine learning to challenging problems in the federal government space. Her undergraduate background is in math and statistics and her senior thesis was on cooperation in evolutionary game theory. Her work is supported by an NSF GRFP award. 

Samir Passi, Information Science

Samir Passi is a PhD candidate in the Department of Information Science at Cornell University. His dissertation, situated within critical data studies, unpacks the human work involved in data science learning, research, and practice. Instances of such work range from the conceptualization of data-driven questions and pre-processing of datasets to translating between corporate values and computational goals and the work of managing corporate data science projects. He studies such forms of work ethnographically in academic (data science education and research) and corporate (corporate data science teams) contexts.

Relevant work includes Data Vision: Learning to See Through Algorithmic Abstraction.

Manish Raghavan, Computer Science

Manish Raghavan is a PhD candidate in the Department of Computer Science at Cornell University, advised by Jon Kleinberg. His primary research interests lie in the application of computational techniques to domains of social concern, including algorithmic fairness and behavioral economics. His work is supported by a NSF GRFP award and Microsoft Research PhD Fellowship.

Relevant work includes How Do Classifiers Induce Agents To Invest Effort Strategically?Inherent Trade-Offs in the Fair Determination of Risk Scores, On Fairness and Calibration, and Selection Problems in the Presence of Implicit Bias.

Sarah Riley, Information Science

Sarah is a PhD student in the Department of Information Science at Cornell University. Her research focuses on bias in automated decision-making. She is interested in computational methods and policy interventions for identifying and mitigating bias.

Sarah has a bachelor’s degree from Amherst College and a master’s in public policy from the University of California, Berkeley. 

Briana Vecchione, Information Science

Briana Vecchione is a PhD student in the Department of Information Science at Cornell University. Her research interests lie broadly in algorithmic fairness and transparency. Prior to Cornell, Briana worked on data science and engineering at Microsoft in NYC. 

Relevant work includes Datasheets for Datasets

Angela Zhou, Operations Research and Information Engineering

Angela Zhou is a third-year PhD student in the Department of Operations Research and Information Engineering. She is based at Cornell Tech and is, advised by Nathan Kallus. Her research focuses on data-driven decision making, in particular leveraging causal inference and optimization to recommend better decisions from observational data. She is particularly interested in methods addressing the unique considerations of applications in healthcare and policy analysis. Her work is supported by a NDSEG graduate fellowship.

Relevant work includes Confounding-Robust Policy Improvement and Residual Unfairness in Fair Machine Learning from Prejudiced Data