Implementing Fair Regression In The Real World
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Abstract (arXiv)
Most fair regression algorithms mitigate bias towards sensitive sub populations and therefore improve fairness at group level. In this paper, we investigate the impact of such implementation of fair regression on the individual. More precisely, we assess the evolution of continuous predictions from an unconstrained to a fair algorithm by comparing results from baseline algorithms with fair regression algorithms for the same data points. Based on our findings, we propose a set of post-processing algorithms to improve the utility of the existing fair regression approaches.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 9 Apr 2021.
Builds on
11 of 24 references.
References · 11
- 2016Equality of Opportunity in Supervised Learningcited by 2,000
- 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
- 2017On Fairness and Calibrationcited by 406
- 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
- 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
- 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
- 2017A Convex Framework for Fair Regressioncited by 147
- 2018A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indicescited by 97
- 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 66
- 2017Fair Kernel Learningcited by 39
- 2019Fairness-Aware Neural R'eyni Minimization for Continuous Featurescited by 15
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Citing papers per year: 2022: 1.
1 paper.
- 2022Data Representativity for Machine Learning and AI Systemscited by 15