Counterfactual Explanations for Ranking in Algorithmic Hiring

Artificial Intelligence (AI) in algorithmic hiring introduces significant challenges related to transparency and interpretability for a diverse set of stakeholders, including job applicants, recruiters, and developers. To address these challenges, Explainable AI (XAI) techniques, such as counterfactual (CF) explanations, can be leveraged to illustrate how small changes in a candidate's profile might influence their ranking outcome. This paper proposes a CF explanation framework specifically designed for ranking algorithms within hiring contexts. The framework empowers stakeholders to better understand the impact of individual profile features on ranking decisions. Our approach distinguishes between different domains relevant to interpreting CFs, offering a structured lens through which explanations can be analyzed and communicated. By generating minimal, actionable modifications to candidate profiles, the framework enhances interpretability in a domain where fairness and transparency are critical. We validate our framework using a novel synthetic hiring dataset, demonstrating its capacity to generate valuable insights for candidates and to support informed decision-making for recruiters and developers.