Cynthia Dwork
· Gordon McKay Professor of Computer ScienceHarvard University · Computer Science
Active 1982–2026
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About
Cynthia Dwork is the Gordon McKay Professor of Computer Science at Harvard University, affiliated with the Harvard Law School and the Harvard Faculty of Arts and Sciences, Department of Statistics. She is the founder of the Hire Aspirations Institute and specializes in applied mathematics, the theory of computation, artificial intelligence, machine learning, and computational and data science. Her research areas include the science, technology, innovation, and public policy related to computer science and AI. Dwork has been recognized for her contributions to the field of computer science, notably receiving the 2026 Japan Prize for her work towards an ethical digital society and the National Medal of Science for her visionary contributions to computer science. Her work has also significantly impacted the development of data privacy and fairness in AI-powered systems.
Research topics
- Computer Science
- Mathematics
- Artificial Intelligence
- Statistics
- Risk analysis (engineering)
- Engineering
- Theoretical computer science
- Machine Learning
- Business
- Algorithm
Selected publications
2021 · 23 citations
1st authorCorrespondingPrediction algorithms assign numbers to individuals that are popularly understood as individual “probabilities”—what is the probability of 5-year survival after cancer diagnosis?—and which increasingly form the basis for life-altering decisions. Drawing on an understanding of computational indistinguishability developed in complexity theory and cryptography, we introduce Outcome Indistinguishability. Predictors that are Outcome Indistinguishable (OI) yield a generative model for outcomes that ca…
Individual Fairness in Pipelines
2020 · 14 citations
1st authorCorrespondingIt is well understood that a system built from individually fair components may not itself be individually fair. In this work, we investigate individual fairness under pipeline composition. Pipelines differ from ordinary sequential or repeated composition in that individuals may drop out at any stage, and classification in subsequent stages may depend on the remaining "cohort" of individuals. As an example, a company might hire a team for a new project and at a later point promote the highest pe…
Individual Fairness in Pipelines
arXiv (Cornell University) · 2020 · 12 citations
1st authorCorrespondingIt is well understood that a system built from individually fair components may not itself be individually fair. In this work, we investigate individual fairness under pipeline composition. Pipelines differ from ordinary sequential or repeated composition in that individuals may drop out at any stage, and classification in subsequent stages may depend on the remaining "cohort" of individuals. As an example, a company might hire a team for a new project and at a later point promote the highest pe…
arXiv (Cornell University) · 2020 · 11 citations
We investigate the power of censoring techniques, first developed for learning {\em fair representations}, to address domain generalization. We examine {\em adversarial} censoring techniques for learning invariant representations from multiple "studies" (or domains), where each study is drawn according to a distribution on domains. The mapping is used at test time to classify instances from a new domain. In many contexts, such as medical forecasting, domain generalization from studies in populou…
Content Moderation and the Formation of Online Communities: A Theoretical Framework
2024-05-08 · 5 citations
articleOpen access1st authorCorrespondingWe study the impact of content moderation policies in online communities. In our theoretical model, a platform chooses a content moderation policy and individuals choose whether or not to participate in the community according to the fraction of user content that aligns with their preferences. The effects of content moderation, at first blush, might seem obvious: platform speech is restricted. However, when user participation decisions are taken into account, its effects can be more subtle --- a…
Recent grants
AF: Medium: Collaborative Proposal: Foundations of Adaptive Data Analysis
NSF · $286k · 2018–2021
Frequent coauthors
- 37 shared
Muli Safra
- 36 shared
Eugène van Heyst
Karlsruhe University of Education
- 36 shared
Kaoru Kurosawa
- 36 shared
Van Linden
FIZ Karlsruhe – Leibniz Institute for Information Infrastructure
- 36 shared
Ong-Schnorr-Shamir Signature
Utrecht University
- 36 shared
C. H. Bennett
Scottish Environment Protection Agency
- 36 shared
Luís Tavares
Centre for Research in Anthropology
- 36 shared
D.L. Ziegler
Labs
Harvard John A. Paulson School of Engineering and Applied SciencesPI
Awards & honors
- 2026 Japan Prize
- National Medal of Science (2025)
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