Explainable Multi-Stakeholder Job Recommender Systems

TL;DR

This paper discusses the importance of explainability, fairness, and privacy in multi-stakeholder job recommender systems, emphasizing the need for transparency and stakeholder-specific requirements in high-stakes recruitment scenarios.

Contribution

It summarizes current research on explainable, multi-stakeholder job recommender systems and outlines future research directions in this critical area.

Findings

Highlighting the importance of explainability in recruitment systems

Identifying stakeholder-specific requirements for fairness and privacy

Proposing future research directions for improved system design

Abstract

Public opinion on recommender systems has become increasingly wary in recent years. In line with this trend, lawmakers have also started to become more critical of such systems, resulting in the introduction of new laws focusing on aspects such as privacy, fairness, and explainability for recommender systems and AI at large. These concepts are especially crucial in high-risk domains such as recruitment. In recruitment specifically, decisions carry substantial weight, as the outcomes can significantly impact individuals' careers and companies' success. Additionally, there is a need for a multi-stakeholder approach, as these systems are used by job seekers, recruiters, and companies simultaneously, each with its own requirements and expectations. In this paper, I summarize my current research on the topic of explainable, multi-stakeholder job recommender systems and set out a number of future research directions.