Fair Single Index Model-Reference-Cited by-同舟云学术

Fair Single Index Model

Published:2024-08-30 Issue: Volume: Page:
ISSN:1556-4681
Container-title:ACM Transactions on Knowledge Discovery from Data
language:en
Short-container-title:ACM Trans. Knowl. Discov. Data

Author:

Wang Yidong¹^ORCID,Ding Meng²^ORCID,Xu Jinhui²^ORCID,Wang Di³^ORCID

Affiliation:

1. The Hong Kong Polytechnic University, China

2. The State University of New York at Buffalo, USA

3. King Abdullah University of Science and Technology, Saudi Arabia

Abstract

Single-index models (SIMs) have been widely used in various applications due to their simplicity and interpretability. However, despite the potential for SIMs to result in discriminatory outcomes based on sensitive attributes like gender, race, or ethnicity, the issue of fairness has not been thoroughly examined in recent studies on the topic. This paper aims to address these fairness concerns by proposing methods for building fair SIMs. Specifically, based on the definition of equal opportunity, we first provide a fairness definition for SIM. Next, we develop a unified fair SIM model and propose an efficient method to solve the fair SIM. Theoretically, we also show that our output is consistent in fairness. Finally, we conduct comprehensive experimental studies over 7 benchmark datasets and demonstrate that our fair SIM outperforms the other 8 baseline methods.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3690646

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