Welcome to the Center for Responsible AI at New York University

Our goal is to build a future in which responsible AI is synonymous with AI. Our work centers around interdisciplinary research, technology policy, and education and training for AI practitioners, decision makers, and the public at large.

What is responsible AI? We use this terms to refer to making the design, development and use of AI socially sustainable: using technology for good while controlling the risks. Responsible AI is about respecting human values, ensuring fairness, maintaining transparency, and upholding accountability. It’s about taking hype and magical thinking out of the conversation about AI. And about giving people the ability to understand, control and take responsibility for AI-assisted decisions.

News
Dec 23, 2023 Juia Stoyanovich published an opinion article in The Hill with recommendations for the US Congress regarding high-impact AI regulation in 2024.
Dec 4, 2023 Join us for the upcoming Fall 2023 Responsible AI (RAI) Research Program Showcase on Friday, December 15th. Learn more about the event.
Dec 4, 2023 We are running a free algorithmic transparency playbook workshop at the NYU Tandon Future Labs on December 12. Learn more about the workshop and RSVP to attend!
Nov 8, 2023 Juia Stoyanovich spoke at the 2023 IBM Security Summit | AI: Today’s Cybersecurity Battleground
Nov 1, 2023 Julia Stoyanovich participated in the US Senate AI Insight Forum on High Impact AI. :sparkles:
Read her statement and learn more about this event.
Nov 1, 2023 Andrew Bell presented Setting the Right Expectations: Algorithmic Recourse Over Time at EAAMO 2023 in Boston. The paper received the Best Paper in the AI Track award! :sparkles:
Selected Publications
  1. Responsible AI literacy: A stakeholder-first approach
    Daniel Dominguez, and Julia Stoyanovich
    Big Data and Society 2023
  2. Setting the Right Expectations: Algorithmic Recourse Over Time
    João Fonseca, Andrew Bell, Carlo Abrate, Francesco Bonchi, and Julia Stoyanovich
    In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO 2023, Boston, MA, USA, 30 October 2023 - 1 November 2023 2023
  3. Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy
    Lucas Rosenblatt, Bernease Herman, Anastasia Holovenko, Wonkwon Lee, Joshua R. Loftus, Elizabeth Mckinnie, Taras Rumezhak, Andrii Stadnik, Bill Howe, and Julia Stoyanovich
    Proc. VLDB Endow. 2023
  4. Fairness in Ranking, Part I: Score-Based Ranking
    Meike Zehlike, Ke Yang, and Julia Stoyanovich
    ACM Computing Surveys 2023
  5. Responsible Data Management
    Julia Stoyanovich, Serge Abiteboul, Bill Howe, H. V. Jagadish, and Sebastian Schelter
    Communications of the ACM 2022
  6. Counterfactuals for the Future
    Lucius E. J. Bynum, Joshua R. Loftus, and Julia Stoyanovich
    In Proceedings of the AAAI Conference on Artificial Intelligence 2023
  7. Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines
    Falaah Arif Khan, Eleni Manis, and Julia Stoyanovich
    In Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO 2022, Arlington, VA, USA, October 6-9, 2022 2022