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
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
@inproceedings{DBLP:conf/eaamo/FonsecaBABS23,author={Fonseca, Jo{\~{a}}o and Bell, Andrew and Abrate, Carlo and Bonchi, Francesco and Stoyanovich, Julia},title={Setting the Right Expectations: Algorithmic Recourse Over Time},booktitle={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},pages={29:1--29:11},publisher={{ACM}},year={2023},url={https://doi.org/10.1145/3617694.3623251},doi={10.1145/3617694.3623251},timestamp={Thu, 09 Nov 2023 21:12:58 +0100},biburl={https://dblp.org/rec/conf/eaamo/FonsecaBABS23.bib},bibsource={dblp computer science bibliography, https://dblp.org},keywords={conference, fairness},}
The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice
Andrew Bell, Lucius Bynum, Nazarii Drushchak, Tetiana Herasymova, Lucas Rosenblatt, and Julia Stoyanovich
Proceedings of the Conference on Fairness, Accountability, and Transparency (ACM FAccT) 2023
@article{bell2023possibility,title={The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice},author={Bell, Andrew and Bynum, Lucius and Drushchak, Nazarii and Herasymova, Tetiana and Rosenblatt, Lucas and Stoyanovich, Julia},journal={Proceedings of the Conference on Fairness, Accountability, and Transparency (ACM FAccT)},year={2023},keywords={fairness, theory, impossibility}}
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 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023 2023
@inproceedings{DBLP:conf/chi/BellNS23,author={Bell, Andrew and Nov, Oded and Stoyanovich, Julia},title={The Algorithmic Transparency Playbook: {A} Stakeholder-first Approach
to Creating Transparency for Your Organization's Algorithms},booktitle={Extended Abstracts of the 2023 {CHI} Conference on Human Factors in
Computing Systems, {CHI} {EA} 2023, Hamburg, Germany, April 23-28,
2023},pages={554:1--554:4},publisher={{ACM}},year={2023},site={https://r-ai.co/transparency-playbook},doi={10.1145/3544549.3574169},keywords={panel,policy,explanability,education,playbook,governance},addendum={peer-reviewed course},author+an={1=self;3=self}}
Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance
@article{bell_nov_stoyanovich_2023,author={Bell, Andrew and Nov, Oded and Stoyanovich, Julia},title={Think About the Stakeholders First! {T}owards an Algorithmic Transparency
Playbook for Regulatory Compliance},volume={5},journal={Data \& Policy},publisher={Cambridge University Press},year={2023},keywords={journal,policy,explainability,education,playbook,governance},}
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
@inproceedings{DBLP:conf/fat/BellSNS22,author={Bell, Andrew and Solano{-}Kamaiko, Ian and Nov, Oded and Stoyanovich, Julia},title={It's Just Not That Simple: An Empirical Study of the Accuracy-Explainability
Trade-off in Machine Learning for Public Policy},booktitle={Proceedings of the 5th Annual {ACM} Conference on Fairness, Accountability, and
Transparency, {FAccT}},pages={248--266},publisher={{ACM}},year={2022},doi={10.1145/3531146.3533090},timestamp={Wed, 22 Jun 2022 10:20:30 +0200},bibsource={dblp computer science bibliography, https://dblp.org},keywords={explainability, transparency, policy,governance},}
The Algorithmic Transparency Playbook: A Stakeholder-first Approach to Creating Transparency for Your Organization’s AlgorithmsIn Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023, Hamburg, Germany, April 23-28, 2023 2023
