Humble AI in the real-world: the case of algorithmic hiring

TL;DR

This paper explores the application of humble AI principles in algorithmic hiring, emphasizing cautiousness, curiosity, and commitment to improve fairness and trust through uncertainty quantification and user experience design.

Contribution

It presents a real-world case study demonstrating how humble AI can be implemented in hiring platforms using uncertainty measures and user-centered approaches.

Findings

Feasibility of uncertainty quantification in ranking algorithms

Enhanced transparency through entropy estimates

Potential for increased trust via user experience design

Abstract

Humble AI (Knowles et al., 2023) argues for cautiousness in AI development and deployments through scepticism (accounting for limitations of statistical learning), curiosity (accounting for unexpected outcomes), and commitment (accounting for multifaceted values beyond performance). We present a real-world case study for humble AI in the domain of algorithmic hiring. Specifically, we evaluate virtual screening algorithms in a widely used hiring platform that matches candidates to job openings. There are several challenges in misrecognition and stereotyping in such contexts that are difficult to assess through standard fairness and trust frameworks; e.g., someone with a non-traditional background is less likely to rank highly. We demonstrate technical feasibility of how humble AI principles can be translated to practice through uncertainty quantification of ranks, entropy estimates, and a user experience that highlights algorithmic unknowns. We describe preliminary discussions with focus groups made up of recruiters. Future user studies seek to evaluate whether the higher cognitive load of a humble AI system fosters a climate of trust in its outcomes.