Andrew Bell

PhD Candidatealb9742(at) nyu.edu

Andrew Bell is a Computer Science Ph.D. Candidate being co-advised by Prof. Julia Stoyanovich and Dr. Oded Nov. Andrew is a recipient of the National Science Foundation Graduate Research Fellowship (NSF GRFP). His research interests lie at the intersection of machine learning and public policy and are more narrowly focused on the fairness and explainability of algorithmic decision systems. In Spring 2023, Andrew was a visiting research fellow at the Center for AI (CENTAI) in Turin, Italy.

In the past, he was a researcher at Data Science for Social Good (DSSG), where he worked on major policy projects like working with a European country’s National Institute of Public Health to develop predictive models that can identify children at risk of not being vaccinated for Measles, Mumps and Rubella. Andrew has also worked at Solve for Good, and the policy research institute MDRC. He graduated from the Calhoun Honors College at Clemson University with a Bachelor’s degree in Mathematics in 2015. Andrew’s interests outside of work include traveling and art.

Selected publications

  1. The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice
    Andrew Bell, Lucius Bynum, Nazarii Drushchak, Tetiana Zakharchenko, Lucas Rosenblatt, and Julia Stoyanovich
    In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, FAccT, Chicago, IL, USA 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. The Algorithmic Transparency Playbook: A Stakeholder-first Approach to Creating Transparency for Your Organization’s Algorithms
    Andrew Bell, Oded Nov, and Julia Stoyanovich
    In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023, Hamburg, Germany, April 23-28, 2023 2023
  4. Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance
    Andrew Bell, Oded Nov, and Julia Stoyanovich
    Data & Policy 2023
  5. It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy
    Andrew Bell, Ian Solano-Kamaiko, Oded Nov, and Julia Stoyanovich
    In Proceedings of the 5th Annual ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
  6. The Game Of Recourse: Simulating Algorithmic Recourse over Time to Improve Its Reliability and Fairness
    Andrew Bell, João Fonseca, and Julia Stoyanovich
    In Companion of the 2024 International Conference on Management of Data, SIGMOD/PODS 2024, Santiago AA, Chile, June 9-15, 2024 2024