We present a new multi-objective optimization approach for synthesizing interpretations that
“explain” the behavior of black-box machine learning models. Constructing human-
understandable interpretations for black-box models often requires balancing conflicting
objectives. A simple interpretation may be easier to understand for humans while being less
precise in its predictions vis-a-vis a complex interpretation. Existing methods for synthesizing
interpretations use a single objective function and are often optimized for a single class of …

We present a new multi-objective optimization approach for synthesizing interpretations that
“explain” the behavior of black-box machine learning models. Constructing
humanunderstandable interpretations for black-box models often requires balancing
conflicting objectives. A simple interpretation may be easier to understand for humans while
being less precise in its predictions vis-a-vis a complex interpretation. Existing methods for
synthesizing interpretations use a single objective function and are often optimized for a …