In this set measurement of gender bias is solely based on the translation of occupations.
Such near misses have been proposed by Winston (1970) as efficient guidance for learning in relational domains.
USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance.
Finally, we quantify these visual explanations based on a bounding-box method with respect to facial regions.
Quick-Shift resulted in the least and Compact-Watershed in the highest correspondence with the reference relevance areas.
First, we show that our approach is capable of identifying a single relation as important explanatory construct.