Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Fair and Explainable Automatic Recruitment

Symbolic AI can translate opaque AI systems into human-readable rules: The paper demonstrates that LFIT (Learning from Interpretation Transition), a symbolic AI technique, can convert black-box machine learning recruitment systems into understandable logical rules, making it possible for hiring managers and job applicants to understand why decisions were made.

Explainability is essential in high-stakes domains like recruitment: The authors emphasize that white-box explanations become "crucial" in sensitive applications involving personal information, particularly when soft biometric data like gender and ethnicity are processed—domains where decisions directly affect people's livelihoods and opportunities.

This approach can help identify and address fairness issues: By converting recruitment algorithms into explicit logical rules, organizations can more easily audit whether their systems are making biased decisions based on protected characteristics, potentially catching discriminatory patterns that remain hidden in traditional machine learning models.

The methodology could extend beyond recruitment: The paper proposes a generalizable scheme applicable to other high-stakes domains mentioned (forensics, e-health, e-learning), suggesting that symbolic AI techniques could improve explainability and fairness across multiple sectors where automated decisions significantly impact individuals.

Technical limitations apply: The authors note LFIT works "under certain conditions," indicating this approach has constraints and may not be universally applicable to all machine learning systems without further development.