Interpretable Data-Based Explanations for Fairness Debugging-Reference-Cited by-同舟云学术

Interpretable Data-Based Explanations for Fairness Debugging

Published:2022-06-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 International Conference on Management of Data
language:
Short-container-title:

Author:

Pradhan Romila¹,Zhu Jiongli²,Glavic Boris³,Salimi Babak²

Affiliation:

1. Purdue University, West Lafayette, IN, USA

2. University of California, San Diego, La Jolla, CA, USA

3. Illinois Institute of Technology, Chicago, IL, USA

Funder

NSF

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3514221.3517886

Reference99 articles.

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3. Housing department slaps facebook with discrimination charge. https://www.npr.org/2019/03/28/707614254/hud-slaps-facebook-with-housing-discrimination-charge , 2019 . Housing department slaps facebook with discrimination charge. https://www.npr.org/2019/03/28/707614254/hud-slaps-facebook-with-housing-discrimination-charge, 2019.

4. Self-driving cars more likely to hit blacks. https://www.technologyreview.com/2019/03/01/136808/self-driving-cars-are-coming-but-accidents-may-not-be-evenly-distributed/ , 2019 . Self-driving cars more likely to hit blacks. https://www.technologyreview.com/2019/03/01/136808/self-driving-cars-are-coming-but-accidents-may-not-be-evenly-distributed/, 2019.

5. Kjersti Aas , Martin Jullum , and Anders Løland . Explaining individual predictions when features are dependent: More accurate approximations to shapley values. arXiv preprint arXiv:1903.10464 , 2019 . Kjersti Aas, Martin Jullum, and Anders Løland. Explaining individual predictions when features are dependent: More accurate approximations to shapley values. arXiv preprint arXiv:1903.10464, 2019.

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