Healthy Friction in Job Recommender Systems

Healthy Friction in Job Recommender Systems

From Data Skeptic by Kyle Polich

February 2, 2026 · 27 min

About this episode

Kyle Polich interviews Roan Schellingerhout about his research on explainable multi-stakeholder recommender systems for job recruitment.

In this episode, host Kyle Polich speaks with Roan Schellingerhout, a fourth-year PhD student at Maastricht University, about explainable multi-stakeholder recommender systems for job recruitment. Roan discusses his research on creating AI-powered job matching systems that balance the needs of multiple stakeholders—job seekers, recruiters, HR professionals, and companies. The conversation explores different types of explanations for job recommendations, including textual, bar chart, and graph-based formats, with findings showing that lay users strongly prefer simple textual explanations over more technical visualizations. Roan shares insights from his "healthy friction" study, which tested whether users could distinguish between real AI-generated explanations and randomly generated ones, revealing that participants often used explanations as information sources rather than decision-making tools. The discussion delves into the technical architecture behind these systems, including the use of knowledge graphs built from tabular data, inference rules, and large language models to generate human-friendly explanations. Roan explains how his research aims to open the black box of…

People in this episode

Host: Kyle Polich

Guest: Roan Schellingerhout

Topics covered

  • job recommender systems
  • AI
  • explainable AI
  • multi-stakeholder systems
  • transparency
  • trustworthiness

Keywords

  • job matching
  • AI explanations
  • knowledge graphs
  • fairness
  • user preferences

Mentioned in this episode

Organizations: Maastricht University

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