Year: 2023
Type: preprint
Abstract: As the data-driven decision process becomes dominating for industrial applications, fairness-aware machine learning arouses great attention in various areas. This work proposes fairness penalties learned by neural networks with a simple random sampler of sensitive attributes for non-discriminatory supervised learning. In contrast to many existing works that critically rely on the discreteness of sensitive attributes and response variables, the proposed penalty is able to handle versatile formats of the sensitive attributes, so it is more extensively applicable in practice than many existing algorithms. This penalty enables us to build a computationally efficient group-level in-processing fairness-aware training framework. Empirical evidence shows that our framework enjoys better utility and fairness measures on popular benchmark data sets than competing methods. We also theoretically characterize estimation errors and loss of utility of the proposed neural-penalized risk minimization problem.
Source: arXiv (Cornell University)
Authors: Sohn, Jinwon, Song, Qifan, Lin, Guang
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: green
Awards: G2778948316, G3047343479, TRUSTWORTHY HETEROGENEOUS DATA-AWARE BAYESIAN FEDERATED LEARNING, Collaborative Research: Robust Deep Learning in Real Physical Space: Generalization, Scalability, and Credibility, Collaborative Research: Inference and Uncertainty Quantification for High Dimensional Systems in Remote Sensing: Methods, Computation, and Applications, G6355769530, ASCENT: From sensors to multiscale digital twin to autonomous operation of resilient electric power grids, Elements: FourPhonon: A Computational Tool for Higher-Order Phonon Anharmonicity and Thermal Properties
OpenAlex ID: W4388650746