Towards A Unifying Human-Centered AI Fairness Framework

Author:

Rahman Munshi Mahbubur1ORCID,Pan Shimei2ORCID,Foulds James R.1ORCID

Affiliation:

1. Department of Information Systems, University of Maryland, Baltimore County, United States of America

2. Department of Information Systems, University of Maryland, Baltimore County, USA

Funder

NFS

Publisher

ACM

Reference32 articles.

1. Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks. ProPublica, May 23 (2016).

2. Ian Ayres. 2002. Outcome tests of racial disparities in police practices. Justice research and Policy 4, 1-2 (2002), 131–142.

3. Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2023. Fairness and Machine Learning: Limitations and Opportunities. MIT Press.

4. Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. 2017. A convex framework for fair regression. 4th Annual Workshop on Fairness, Accountability, and Transparency in Machine Learning. ArXiv preprint arXiv:1706.02409 [cs.LG] (2017).

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