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Explainable Multi-Stakeholder Job Recommender Systems

Roan Schellingerhout

TL;DR

This paper summarizes the current research on the topic of explainable, multi-stakeholder job recommender systems and set out a number of future research directions.

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.

Explainable Multi-Stakeholder Job Recommender Systems

TL;DR

This paper summarizes the current research on the topic of explainable, multi-stakeholder job recommender systems and set out a number of future research directions.

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.
Paper Structure (14 sections, 1 figure)

This paper contains 14 sections, 1 figure.

Table of Contents

  1. Background and Context
  2. Completed research
  3. Stakeholder preferences (SQ1)
  4. Mock-up System Experiment (SQ1)
  5. Explainable Graph Neural Network (SQ2)
  6. Future work
  7. SQ1: Designing desirable explanations
  8. Improving explanation coherence
  9. Clarifying textual explanations
  10. SQ2: Improving model performance
  11. Exploiting linked data
  12. SQ3: Evaluating the system as a whole
  13. Evaluating in a real-world context
  14. List-wise explanations

Figures (1)

  • Figure 1: The interface of the online environment with which the participants interacted. In this screenshot, all explanations are enabled. These can individually be toggled based on the user's preference. The web environment uses exclusively Dutch text, as the interviewees were all native Dutch speakers. The environment consists of the following components: (1) the list of recommended items, which were presented in a randomized order (i.e., the top item was not necessarily the best match); (2) the textual explanation; (3) the bar chart explanation; (4) the graph-based explanation. This example shows a real explanation.