Modeling Fairness in Recruitment AI via Information Flow
This work addresses fairness risks in recruitment AI for stakeholders by providing a structured analysis method, though it is incremental as it builds on existing modeling approaches.
The paper tackled the problem of bias in AI-supported recruitment by applying an information flow-based modeling framework to a real-world process, identifying where biases emerge and propagate through algorithmic and human components to impact candidates.
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.