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Aaron Roth

· Professor

University of Pennsylvania · Computer and Information Science

Active 1999–2025

h-index54
Citations17.7k
Papers381115 last 5y
Funding$5.2M

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Aaron Roth is a professor whose research focuses on privacy, fairness, and security in computer science. His work involves developing algorithms and systems that ensure data privacy and equitable treatment in computational processes. Roth has contributed to the understanding of how to design algorithms that respect user privacy while maintaining utility, and he has worked on issues related to algorithmic fairness and security. His background includes extensive research in theoretical computer science, with a particular emphasis on the intersection of privacy and machine learning. Roth's key contributions include advancing the theoretical foundations of differential privacy and exploring its applications in real-world systems. His research aims to create practical solutions that balance data utility with privacy guarantees, and to address ethical concerns related to algorithmic decision-making.

Research topics

  • Computer Science
  • Artificial Intelligence
  • Sociology
  • Machine Learning
  • Algorithm
  • Data Mining
  • Political Science
  • Database
  • Econometrics
  • Operating system

Selected publications

  • A snapshot of the frontiers of fairness in machine learning

    Communications of the ACM · 2020 · 338 citations

    Senior authorCorresponding

    A group of industry, academic, and government experts convene in Philadelphia to explore the roots of algorithmic bias.

    DOI

  • The Ethical Algorithm: The Science of Socially Aware Algorithm Design

    Perspectives on Science and Christian Faith · 2021 · 266 citations

    Senior authorCorresponding

    THE ETHICAL ALGORITHM: The Science of Socially Aware Algorithm Design by Michael Kearns and Aaron Roth. New York: Oxford University Press, 2019. 232 pages. Hardcover; $24.95. ISBN: 9780190948207. *Can an algorithm be ethical? That question appears to be similar to asking if a hammer can be ethical. Isn't the ethics solely related to how the hammer is used? Using it to build a house seems ethical; using it to harm another person would be immoral. *That line of thinking would be appropriate if the…

    DOI

  • The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review

    Journal of the American Statistical Association · 2025-06-02 · 2 citations

    article
    PublisherDOI

  • Algorithmic Collusion Without Threats

    arXiv (Cornell University) · 2024-09-06 · 2 citations

    preprintOpen access

    There has been substantial recent concern that pricing algorithms might learn to ``collude.'' Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing games, in which sellers play strategies which threaten to punish their competitors who refuse to support high prices, and these strategies can be automatically learned. In fact, a standard economic intuition is that supra-competitive prices emerge from either the use of threats, or a failure of one party to optimize their payo…

    PublisherOA PDFDOI

  • Best vs. All: Equity and Accuracy of Standardized Test Score Reporting

    arXiv (Cornell University) · 2021 · 2 citations

    We study a game theoretic model of standardized testing for college admissions. Students are of two types; High and Low. There is a college that would like to admit the High type students. Students take a potentially costly standardized exam which provides a noisy signal of their type. The students come from two populations, which are identical in talent (i.e. the type distribution is the same), but differ in their access to resources: the higher resourced population can at their option take the…

    PublisherOA PDF

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