Fair Sequential Selection Using Supervised Learning Models

TL;DR

This paper examines fairness in sequential selection processes, revealing limitations of existing fairness notions and proposing a new fairness criterion, Equal Selection, along with a post-processing method to achieve it, even with privacy constraints.

Contribution

The paper introduces the Equal Selection fairness notion tailored for sequential selection problems and develops a post-processing approach to enforce it, addressing limitations of traditional fairness measures.

Findings

Existing fairness notions may still lead to biased outcomes in sequential selection.

The proposed Equal Selection fairness can be achieved through a post-processing method.

Fairness can be maintained even with noisy sensitive attribute data under certain conditions.

Abstract

We consider a selection problem where sequentially arrived applicants apply for a limited number of positions/jobs. At each time step, a decision maker accepts or rejects the given applicant using a pre-trained supervised learning model until all the vacant positions are filled. In this paper, we discuss whether the fairness notions (e.g., equal opportunity, statistical parity, etc.) that are commonly used in classification problems are suitable for the sequential selection problems. In particular, we show that even with a pre-trained model that satisfies the common fairness notions, the selection outcomes may still be biased against certain demographic groups. This observation implies that the fairness notions used in classification problems are not suitable for a selection problem where the applicants compete for a limited number of positions. We introduce a new fairness notion, ``Equal Selection (ES),'' suitable for sequential selection problems and propose a post-processing approach to satisfy the ES fairness notion. We also consider a setting where the applicants have privacy concerns, and the decision maker only has access to the noisy version of sensitive attributes. In this setting, we can show that the perfect ES fairness can still be attained under certain conditions.