A Novel Regularization Approach to Fair ML
Not on the map. Why?
Abstract (arXiv)
A number of methods have been introduced for the fair ML issue, most of them complex and many of them very specific to the underlying ML moethodology. Here we introduce a new approach that is simple, easily explained, and potentially applicable to a number of standard ML algorithms. Explicitly Deweighted Features (EDF) reduces the impact of each feature among the proxies of sensitive variables, allowing a different amount of deweighting applied to each such feature. The user specifies the deweighting hyperparameters, to achieve a given point in the Utility/Fairness tradeoff spectrum. We also introduce a new, simple criterion for evaluating the degree of protection afforded by any fair ML method.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 25 references that are in the map. First posted on arXiv 13 Aug 2022.
Builds on
8 of 25 references.
References · 8
- 2015ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and Rcited by 171
- 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
- 2019Inherent Tradeoffs in Learning Fair Representationscited by 144
- 2020On the Optimal Weighted ℓ2 Regularization in Overparameterized Linear Regressioncited by 97
- 2021It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarkscited by 61
- 2019A simple measure of conditional dependencecited by 38
- 2020A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Setcited by 35
- 2021Achieving Fairness with a Simple Ridge Penaltycited by 13
Built on by
Citing papers per year: 2024: 2; 2025: 1.
3 papers.