Fairness through Aleatoric Uncertainty

Author:

Tahir Anique1ORCID,Cheng Lu2ORCID,Liu Huan1ORCID

Affiliation:

1. Arizona State University, Tempe, AZ, USA

2. University of Illinois Chicago, Chicago, IL, USA

Funder

National Science Foundation

Publisher

ACM

Reference55 articles.

1. A review of uncertainty quantification in deep learning: Techniques, applications and challenges

2. Abien Fred Agarap. 2018. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375. Abien Fred Agarap. 2018. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375.

3. Alekh Agarwal , Alina Beygelzimer , Miroslav Dudik , John Langford , and Hanna Wallach . 2018 . A reductions approach to fair classification . In International Conference on Machine Learning. PMLR, 60--69 . Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach. 2018. A reductions approach to fair classification. In International Conference on Machine Learning. PMLR, 60--69.

4. Rachel K. E. Bellamy et al . 2018 . AI Fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. (Oct. 2018). https://arxiv.org/abs/1810.01943. Rachel K. E. Bellamy et al. 2018. AI Fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. (Oct. 2018). https://arxiv.org/abs/1810.01943.

5. Coalition-Proof Nash Equilibria I. Concepts

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