Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair and Explainable Automatic Recruitment
This work addresses the need for human-readable explanations in sensitive AI applications like recruitment, which is crucial for ensuring fairness and transparency for job applicants and employers. It is an incremental step towards a general methodology for incorporating declarative explanations into machine learning.
This paper explores the viability of Learning from Interpretation Transition (LFIT), an Inductive Logic Programming technique, to generate white-box explanations for machine learning systems. It applies LFIT to a fair recruitment scenario, specifically for ranking Curricula Vitae using soft biometric information, demonstrating its expressiveness for this problem.
Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods can become crucial. Inductive Logic Programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the process of data. Learning from Interpretation Transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains.