Sorelle A. Friedler
Sorelle A. Friedler
Shibulal Family Professor of Computer Science, Haverford College
Verified email at - Homepage
Cited by
Cited by
Certifying and removing disparate impact
M Feldman, SA Friedler, J Moeller, C Scheidegger, ...
proceedings of the 21th ACM SIGKDD international conference on knowledge …, 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
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
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
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
Runaway feedback loops in predictive policing
D Ensign, SA Friedler, S Neville, C Scheidegger, S Venkatasubramanian
Conference on Fairness, Accountability, and Transparency, 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
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
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
Hiring by algorithm: predicting and preventing disparate impact
I Ajunwa, S Friedler, CE Scheidegger, S Venkatasubramanian
Available at SSRN, 2016
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: …, 2016
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
Assessing the Local Interpretability of Machine Learning Models
D Slack, SA Friedler, C Scheidegger, CD Roy
NeurIPS Workshop on Human-Centric Machine Learning, 2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
D Slack, S Friedler, E Givental
Conference on Fairness, Accountability, and Transparency, 2020
Energy Usage Reports: Environmental awareness as part of algorithmic accountability
K Lottick, S Susai, SA Friedler, JP Wilson
Workshop on Tackling Climate Change with Machine Learning at NeurIPS 2019, 2019
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
Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People
TWHOST Policy, 2022
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
CodeCarbon: estimate and track carbon emissions from machine learning computing
V Schmidt, K Goyal, A Joshi, B Feld, L Conell, N Laskaris, D Blank, ...
Cited on 20, 2021
How to hold algorithms accountable
N Diakopoulos, S Friedler
MIT Technology Review 17 (11), 2016, 2016
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