InfoGram and admissible machine learning

We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under regulatory constraints. This has led to great interest in developing fair, transparent and trustworthy ML methods. The purpose this article introduce information-theoretic framework (admissible learning) algorithmic risk-management tools (InfoGram, L-features, ALFA-testing) that can guide an analyst redesign off-the-shelf methods compliant, while maintaining good prediction accuracy. illustrated our approach using several real-data examples from financial sectors, biomedical research, marketing campaigns, criminal justice system.