This portal has been archived. Explore the next generation of this technology.
Supervised learning algorithms resilient to discriminatory data perturbations
lib:dab75812b3ef50d6 (v1.0.0)
Authors: Przemyslaw A. Grabowicz,Kenta Takatsu,Luis F. Lafuerza
ArXiv: 1912.08189
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1912.08189v2
The actions of individuals can be discriminatory with respect to certain \textit{protected} attributes, such as race or gender. Recently, discrimination has become a focal concern in supervised learning algorithms augmenting human decision-making. These systems are trained using historical data, which may have been tainted by discrimination, and may learn biases against the protected groups. An important question is how to train models without propagating discrimination. Such discrimination can be either direct, when one or more of protected attributes are used in the decision-making directly, or indirect, when other attributes correlated with the protected attributes are used in an unjustified manner. In this work, we i) model discrimination as a perturbation of data-generating process; ii) introduce a measure of resilience of a supervised learning algorithm to potentially discriminatory data perturbations; and iii) propose a novel supervised learning method that is more resilient to such discriminatory perturbations than state-of-the-art learning algorithms addressing discrimination. The proposed method can be used with general supervised learning algorithms, prevents direct discrimination and avoids inducement of indirect discrimination, while maximizing model accuracy.
ArXiv: 1912.08189
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1912.08189v2
The actions of individuals can be discriminatory with respect to certain \textit{protected} attributes, such as race or gender. Recently, discrimination has become a focal concern in supervised learning algorithms augmenting human decision-making. These systems are trained using historical data, which may have been tainted by discrimination, and may learn biases against the protected groups. An important question is how to train models without propagating discrimination. Such discrimination can be either direct, when one or more of protected attributes are used in the decision-making directly, or indirect, when other attributes correlated with the protected attributes are used in an unjustified manner. In this work, we i) model discrimination as a perturbation of data-generating process; ii) introduce a measure of resilience of a supervised learning algorithm to potentially discriminatory data perturbations; and iii) propose a novel supervised learning method that is more resilient to such discriminatory perturbations than state-of-the-art learning algorithms addressing discrimination. The proposed method can be used with general supervised learning algorithms, prevents direct discrimination and avoids inducement of indirect discrimination, while maximizing model accuracy.
Relevant initiatives
Related knowledge about this paper
Reproduced results (crowd-benchmarking and competitions)
Artifact and reproducibility checklists
- ACM/cTuning artifact appendix and reproducibility checklist
- NeurIPS reproducibility checklist
- SIGPLAN's checklist for empirical evaluation
- Collective Knowledge (organizing research projects based on FAIR principles)