From Skill Extraction to Multistakeholder Recommendation: A Two-Stage Framework for Bias Governance in Skills-Based Job Matching

2026-07-17

AI Summary

This study addresses bias in skill-oriented job matching systems that may undermine hiring fairness. The authors propose a unified two-stage governance framework: in the first stage, a chatbot extracts candidate skills and disentangles hard and soft constraint biases; in the second stage, preferences from candidates, employers, and regulators are integrated through a multi-stakeholder recommendation mechanism grounded in social choice theory. The framework incorporates distributional auditing, counterfactual testing, and dynamic fairness evaluation to enable auditable bias detection. It automatically triggers corrective actions or generates compliance reports when predefined fairness thresholds are violated, thereby significantly enhancing the system’s fairness, transparency, and regulatory alignment—such as with the EU AI Act.

Abstract

AI-based labor-market systems or platforms can affect access to job opportunities prior to organizational candidate rankings or hiring decisions. Such applications warrant caution, as biases in skill extraction, profile formation, and candidate-job matching may contribute to unfair treatment of candidates. In this paper, we propose a two-stage framework for detecting and governing bias in skills-based job matching. Stage 1, skill extraction and profile formation, addresses how candidates provide skills and preferences to the system, how the system extracts and structures this information, and the bias risks this entails, with a focus on chatbot-based elicitation. Stage 2, multistakeholder candidate-job recommendation, would embed this information in a recommender system in which candidate, company, and regulatory objectives are represented by separate agents, each producing an independent candidate-job ranking; these rankings would be combined through social choice-based aggregation into a single, auditable recommendation. The two stages are connected by a shared distinction between hard constraints, which require correction before processing continues, and soft constraints, which are logged to inform later decisions. Following an AI Act-aligned assessment methodology (based on the Fraunhofer AI Assessment Catalog), we propose using distributional auditing and counterfactual testing to produce a Stage 1 bias inventory sorted into hard and soft constraints, with the latter informing fairness thresholds for Stage 2. The same logic would apply to Stage 2: fairness metrics crossing predefined thresholds would trigger an adapted recommendation process, while smaller deviations would be logged as bias reports and persistent fairness states.

Problem

• bias governance
• skill extraction
• job matching
• fairness
• multistakeholder recommendation

Innovation

• bias governance
• two-stage framework
• multistakeholder recommendation
• skill extraction
• social choice aggregation