| Certifying and removing disparate impact M Feldman, SA Friedler, J Moeller, C Scheidegger, ... proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 1634 | 2015 |
| Machine-learning-assisted materials discovery using failed experiments P Raccuglia, KC Elbert, PDF Adler, C Falk, MB Wenny, A Mollo, M Zeller, ... Nature 533 (7601), 73-76, 2016 | 1175 | 2016 |
| Fairness and abstraction in sociotechnical systems AD Selbst, D Boyd, SA Friedler, S Venkatasubramanian, J Vertesi Proceedings of the conference on fairness, accountability, and transparency …, 2019 | 664 | 2019 |
| A comparative study of fairness-enhancing interventions in machine learning SA Friedler, C Scheidegger, S Venkatasubramanian, S Choudhary, ... Proceedings of the conference on fairness, accountability, and transparency …, 2019 | 538 | 2019 |
| On the (im) possibility of fairness SA Friedler, C Scheidegger, S Venkatasubramanian arXiv preprint arXiv:1609.07236, 2016 | 416 | 2016 |
| Runaway feedback loops in predictive policing D Ensign, SA Friedler, S Neville, C Scheidegger, S Venkatasubramanian Conference on Fairness, Accountability, and Transparency, 2018 | 379 | 2018 |
| Auditing black-box models for indirect influence P Adler, C Falk, SA Friedler, T Nix, G Rybeck, C Scheidegger, B Smith, ... Knowledge and Information Systems 54, 95-122, 2018 | 334 | 2018 |
| Problems with Shapley-value-based explanations as feature importance measures IE Kumar, S Venkatasubramanian, C Scheidegger, S Friedler International Conference on Machine Learning, 5491-5500, 2020 | 244 | 2020 |
| Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis X Jia, A Lynch, Y Huang, M Danielson, I Lang’at, A Milder, AE Ruby, ... Nature 573 (7773), 251-255, 2019 | 139 | 2019 |
| The (im) possibility of fairness: Different value systems require different mechanisms for fair decision making SA Friedler, C Scheidegger, S Venkatasubramanian Communications of the ACM 64 (4), 136-143, 2021 | 109 | 2021 |
| Assessing the Local Interpretability of Machine Learning Models D Slack, SA Friedler, C Scheidegger, CD Roy NeurIPS Workshop on Human-Centric Machine Learning, 2019 | 71 | 2019 |
| Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management IM Pendleton, G Cattabriga, Z Li, MA Najeeb, SA Friedler, AJ Norquist, ... MRS Communications 9 (3), 846-859, 2019 | 68 | 2019 |
| Principles for accountable algorithms and a social impact statement for algorithms N Diakopoulos, S Friedler, M Arenas, S Barocas, M Hay, B Howe, ... Dagstuhl working group write-up: https://www.fatml.org/resources/principles …, 2016 | 68 | 2016 |
| Hiring by algorithm: predicting and preventing disparate impact I Ajunwa, S Friedler, CE Scheidegger, S Venkatasubramanian Available at SSRN, 2016 | 64 | 2016 |
| How to hold algorithms accountable N Diakopoulos, S Friedler MIT Technology Review 17 (11), 2016, 2016 | 53 | 2016 |
| Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data D Slack, S Friedler, E Givental Conference on Fairness, Accountability, and Transparency, 2020 | 45 | 2020 |
| Fairness in representation: quantifying stereotyping as a representational harm M Abbasi, SA Friedler, C Scheidegger, S Venkatasubramanian Proceedings of the 2019 SIAM International Conference on Data Mining, 801-809, 2019 | 44 | 2019 |
| Gaps in Information Access in Social Networks B Fish, A Bashardoust, D Boyd, S Friedler, C Scheidegger, ... The World Wide Web Conference, 480-490, 2019 | 42 | 2019 |
| Automated congressional redistricting HA Levin, SA Friedler Journal of Experimental Algorithmics (JEA) 24, 1-24, 2019 | 37 | 2019 |
| Shapley Residuals: Quantifying the limits of the Shapley value for explanations I Kumar, C Scheidegger, S Venkatasubramanian, S Friedler Advances in Neural Information Processing Systems 34, 26598-26608, 2021 | 28 | 2021 |
Suresh VenkatasubramanianBrown UniversityVerified email at brown.edu
