Using Self-supervised Learning Can Improve Model Fairness

Author:

Yfantidou Sofia1ORCID,Spathis Dimitris2ORCID,Constantinides Marios2ORCID,Vakali Athena1ORCID,Quercia Daniele2ORCID,Kawsar Fahim2ORCID

Affiliation:

1. Aristotle University of Thessaloniki, Thessaloniki, Greece

2. Nokia Bell Labs, Cambridge, United Kingdom

Funder

Marie Sk?odowska-Curie grant agreement Innovative Training Networks (ITN) - RAIS No 813162.

Publisher

ACM

Reference63 articles.

1. Rahn Kennedy Bailey, Josephine Mokonogho, and Alok Kumar. 2019. Racial and ethnic differences in depression: current perspectives. Neuropsychiatric disease and treatment (2019), 603--609.

2. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

3. Tal Burt, KS Button, HHZ Thom, RJ Noveck, and Marcus R Munafò. 2017. The Burden of the ?False-Negatives" in Clinical Development: Analyses of Current and Alternative Scenarios and Corrective Measures. Clinical and translational science, Vol. 10, 6 (2017), 470--479.

4. Irene Chen, Fredrik D Johansson, and David Sontag. 2018. Why is my classifier discriminatory? Advances in neural information processing systems, Vol. 31 (2018).

5. Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607.

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