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 Torino, 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
Estimating the impact of the Russian invasion on the displacement of graduating high school students in Ukraine
Tetiana Zakharchenko, Andrew Bell, Nazarii Drushchak, Oleksandra Konopatska, Falaah Arif Khan, and Julia Stoyanovich
Nature Humanities and Social Sciences Communications 2025
@article{ukraine_edu25,title={Estimating the impact of the {Russian} invasion on the displacement of graduating high school students in {Ukraine}},author={Zakharchenko, Tetiana and Bell, Andrew and Drushchak, Nazarii and Konopatska, Oleksandra and Khan, Falaah Arif and Stoyanovich, Julia},year={2025},journal={Nature Humanities and Social Sciences Communications},keywords={journal,education,policy,RAIforUkraine},}
SHAP-based Explanations are Sensitive to Feature Representation
Hyunseung Hwang, Andrew Bell, Joao Fonseca, Venetia Pliatsika, Julia Stoyanovich, and Steven Euijong Whang
In Conference on Fairness, Accountability, and Transparency, ACM FAccT 2025
@inproceedings{sharp_attack,title={{SHAP}-based Explanations are Sensitive to Feature Representation},author={Hwang, Hyunseung and Bell, Andrew and Fonseca, Joao and Pliatsika, Venetia and Stoyanovich, Julia and Whang, Steven Euijong},year={2025},booktitle={Conference on Fairness, Accountability, and Transparency, {ACM FAccT}},keywords={conference,data,explainability,transparency}}
SafeNudge: Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs
Joao Fonseca, Andrew Bell, and Julia Stoyanovich
In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP) Nov 2025
@inproceedings{fonseca-etal-2025-safenudge,title={{SafeNudge}: Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs},author={Fonseca, Joao and Bell, Andrew and Stoyanovich, Julia},booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP)},month=nov,year={2025},publisher={ACM},doi={10.18653/v1/2025.emnlp-main.1010},pages={19955--19969},isbn={979-8-89176-332-6},keywords={conference,privacy,safety}}
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
Natalia Ponomareva, Zheng Xu, H Brendan McMahan, Peter Kairouz, Lucas Rosenblatt, Vincent Cohen-Addad, Cristóbal Guzmán, Ryan McKenna, Galen Andrew, Alex Bie, and others
@article{ponomareva2025dp,title={How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy},author={Ponomareva, Natalia and Xu, Zheng and McMahan, H Brendan and Kairouz, Peter and Rosenblatt, Lucas and Cohen-Addad, Vincent and Guzm{\'a}n, Crist{\'o}bal and McKenna, Ryan and Andrew, Galen and Bie, Alex and others},journal={CoRR},year={2025},keywords={working,privacy}}
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 Nov 2023
@inproceedings{DBLP:conf/fat/BellBDZRS23,author={Bell, Andrew and Bynum, Lucius and Drushchak, Nazarii and Zakharchenko, Tetiana and Rosenblatt, Lucas and Stoyanovich, Julia},title={The Possibility of Fairness: Revisiting the Impossibility Theorem
in Practice},booktitle={Proceedings of the {ACM} Conference on Fairness, Accountability,
and Transparency, FAccT, Chicago, IL, USA},pages={400--422},publisher={{ACM}},year={2023},doi={10.1145/3593013.3594007},keywords={conference,fairness,RAIforUkraine},author+an={1=self;2=self;3=self;4=self;5=self;6=self}}
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 Nov 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}}
How Much Effort Is Enough? Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity
Andrew Bell, Joao Fonseca, Carlo Abrate, Francesco Bonchi, and Julia Stoyanovich
In Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO Nov 2025
@inproceedings{fairrecourse,title={How Much Effort Is Enough? Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity},author={Bell, Andrew and Fonseca, Joao and Abrate, Carlo and Bonchi, Francesco and Stoyanovich, Julia},year={2025},booktitle={Equity and Access in Algorithms, Mechanisms, and Optimization, {EAAMO}},publisher={{ACM}},doi={10.1145/3757887.3763014},keywords={conference,fairness}}
Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice
Andrew Bell, and Julia Stoyanovich
In Proceedings of the AAAI Conference on Artificial Intelligence Nov 2025
@inproceedings{bellstoyanovich_eaai25,title={Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice},author={Bell, Andrew and Stoyanovich, Julia},year={2025},volume={39},pages={28964-28972},booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},keywords={conference,education,policy,transparency,playbook},doi={10.1609/aaai.v39i28.35165},url={https://doi.org/10.1609/aaai.v39i28.35165}}
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 Nov 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 Nov 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,conference},}
Making Transparency Influencers: A Case Study of an Educational
Approach to Improve Responsible AI Practices in News and Media
Andrew Bell, and Julia Stoyanovich
In Extended Abstracts of the CHI Conference on Human Factors in Computing
Systems, CHI EA 2024, Honolulu, HI, USA, May 11-16, 2024 Nov 2024
@inproceedings{DBLP:conf/chi/BellS24,author={Bell, Andrew and Stoyanovich, Julia},editor={Mueller, Florian 'Floyd' and Kyburz, Penny and Williamson, Julie R. and Sas, Corina},title={Making Transparency Influencers: {A} Case Study of an Educational
Approach to Improve Responsible {AI} Practices in News and Media},booktitle={Extended Abstracts of the {CHI} Conference on Human Factors in Computing
Systems, {CHI} {EA} 2024, Honolulu, HI, USA, May 11-16, 2024},pages={523:1--523:8},publisher={{ACM}},year={2024},doi={10.1145/3613905.3637113},timestamp={Fri, 17 May 2024 21:42:37 +0200},biburl={https://dblp.org/rec/conf/chi/BellS24.bib},bibsource={dblp computer science bibliography, https://dblp.org},keywords={explainability, transparency, policy,governance},}
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 Nov 2024
@inproceedings{DBLP:conf/sigmod/BellFS24,author={Bell, Andrew and Fonseca, Jo{\~{a}}o and Stoyanovich, Julia},editor={Barcel{\'{o}}, Pablo and Pi, Nayat S{\'{a}}nchez and Meliou, Alexandra and Sudarshan, S.},title={The Game Of Recourse: Simulating Algorithmic Recourse over Time to
Improve Its Reliability and Fairness},booktitle={Companion of the 2024 International Conference on Management of Data,
{SIGMOD/PODS} 2024, Santiago AA, Chile, June 9-15, 2024},pages={464--467},publisher={{ACM}},year={2024},url={https://doi.org/10.1145/3626246.3654742},doi={10.1145/3626246.3654742},keywords={fairness,conference,demo},author+an={1=self;2=self;3=self},}
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 Nov 2023
