Developing a Fair Online Recruitment Framework Based on Job-seekers' Fairness Concerns

TL;DR

This paper addresses fairness in online recruitment by deriving a user-centered framework from job-seekers' concerns expressed in online communities. It combines a qualitative taxonomy with algorithmic and interface design implications, grounded in Value Sensitive Design, to produce a fair recruitment framework spanning sourcing to evaluation. The study uses a RoBERTa-based classifier on a large Reddit dataset to identify fairness-related posts and then develops four core themes—discrimination against sensitive attributes, interaction bias, improper interpretations of qualifications, and power imbalance—each paired with concrete design recommendations. The resulting framework highlights practical, stage-specific interventions, including diversified sensitive attributes, proxy-mitigation, modularized pipelines, nudges toward objective evaluation, and two-sided fairness, to better align computational fairness with real-world job-seekers’ perceptions. Overall, the work connects real-world fairness concerns with actionable design principles to improve both algorithms and interfaces in online hiring, while acknowledging limitations and the need for broader, human-centered validation.

Abstract

The susceptibility to biases and discrimination is a pressing issue in today's labor markets. Though digital recruitment systems play an increasingly significant role in human resources management, thus far we lack a systematic understanding of human-centered design principles for fair online hiring. This work proposes a fair recruitment framework based on job-seekers' fairness concerns shared in an online forum. Through qualitative analysis, we uncover four overarching themes of job-seekers' fairness concerns, including discrimination against sensitive attributes, interaction biases, improper interpretations of qualifications, and power imbalance. Based on these findings, we derive design implications for algorithms and interfaces in recruitment systems, integrating them into a fair recruitment framework spanning different hiring stages and fairness considerations.

Figures (3)

• Figure 1: Descriptive statistics on (a) temporal trend and (b) topic distribution of posts in r/jobs. The count in the y-axis indicates the number of posts in the corresponding month(s) for figure (a) and the number of posts with relevant tags for figure (b).
• Figure 2: A framework for designing fair recruitment algorithms
• Figure 3: A framework for designing fair recruitment interfaces