  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 the regulatory constraints. This has led to great interest
in developing fair, transparent and trustworthy ML methods. The purpose of this
article is to introduce a new information-theoretic learning framework
(admissible machine learning) and algorithmic risk-management tools (InfoGram,
L-features, ALFA-testing) that can guide an analyst to redesign off-the-shelf
ML methods to be regulatory compliant, while maintaining good prediction
accuracy. We have illustrated our approach using several real-data examples
from financial sectors, biomedical research, marketing campaigns, and the
criminal justice system.
