从技能提取到多利益相关方推荐：基于技能的工作匹配中偏见治理的两阶段框架
From Skill Extraction to Multistakeholder Recommendation: A Two-Stage Framework for Bias Governance in Skills-Based Job Matching
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中文总结 AI 辅助
研究基于技能的工作匹配中的偏见治理，提出两阶段框架，第一阶段处理技能提取等，第二阶段进行多利益相关方推荐，通过区分硬软约束相连，用分布审计等方法检测治理偏见，生成清单并设定公平阈值。
中文摘要 AI 辅助
基于人工智能的劳动力市场系统或平台在组织候选人排名或招聘决定之前就可能影响就业机会获取。由于技能提取、档案形成和候选人与工作匹配中的偏见可能导致对候选人的不公平待遇，本文提出一个两阶段框架用于检测和治理基于技能的工作匹配中的偏见。第一阶段聚焦基于聊天机器人的技能提取和档案形成，处理候选人如何提供技能和偏好等问题及其中的偏见风险。第二阶段将信息嵌入多利益相关方推荐系统，各主体产生独立排名后经基于社会选择的聚合形成单一可审计推荐。两阶段通过区分硬约束和软约束相连，遵循与人工智能法案一致的评估方法，用分布审计和反事实测试生成偏见清单，为第二阶段提供公平阈值等。
英文摘要
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.
