A statistical approach to detect disparity prone features in a group fairness setting-Reference-Cited by-同舟云学术

A statistical approach to detect disparity prone features in a group fairness setting

Published:2023-11-14 Issue: Volume: Page:
ISSN:2730-5953
Container-title:AI and Ethics
language:en
Short-container-title:AI Ethics

Author:

Pelegrina Guilherme Dean^ORCID,Couceiro Miguel^ORCID,Duarte Leonardo Tomazeli^ORCID

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

TAILOR

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s43681-023-00363-9.pdf

Reference48 articles.

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3. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635 (2019)

4. Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias-ProPublica (2016). https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Accessed 16 Oct 2023

5. Raji, I.D., Buolamwini, J.: Actionable auditing: investigating the impact of publicly naming biased performance results of commercial AI products. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, Honolulu, HI, USA, pp. 429–435 (2019)

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