  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.
