用信息流建模分析招聘AI中的偏见传播路径
Modeling Fairness in Recruitment AI via Information Flow
- 构建多层级信息流模型，追踪算法与人工决策间的信息变化
- 发现偏见在系统中如何产生并传递，影响候选人结果
- 适合关注AI公平性与系统透明性的研究者和从业者
避免偏见并理解AI辅助决策的真实影响，是实现公平与责任归属的关键。现有方法往往只关注技术层面（如数据集和模型）或高层次的社会伦理问题，很少揭示二者在实践中如何相互作用。本文将基于信息流的建模框架应用于真实招聘流程，该流程结合了自动化候选人匹配与人工决策。通过半结构化利益相关者访谈与迭代建模，我们构建了招聘流程的多层次表示，捕捉算法与人类组件之间信息的转换、过滤与解释过程。识别出偏见可能产生的环节，其在系统中的传播路径，以及对候选人的下游影响。该案例研究展示了信息流建模如何支持对公平性风险的结构化分析，提升复杂人机系统的透明度。
原文摘要 · Abstract (English)
Avoiding bias and understanding the real-world consequences of AI-supported decision-making are critical to address fairness and assign accountability. Existing approaches often focus either on technical aspects, such as datasets and models, or on high-level socio-ethical considerations - rarely capturing how these elements interact in practice. In this paper, we apply an information flow-based modeling framework to a real-world recruitment process that integrates automated candidate matching with human decision-making. Through semi-structured stakeholder interviews and iterative modeling, we construct a multi-level representation of the recruitment pipeline, capturing how information is transformed, filtered, and interpreted across both algorithmic and human components. We identify where biases may emerge, how they can propagate through the system, and what downstream impacts they may have on candidates. This case study illustrates how information flow modeling can support structured analysis of fairness risks, providing transparency across complex socio-technical systems.
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