  In this paper, we propose a general framework for mitigating the disparities
of the predicted classes with respect to secondary attributes within the data
(e.g., race, gender etc.). Our proposed method involves learning a
multi-objective function that in addition to learning the primary objective of
predicting the primary class labels from the data, also employs a
clustering-based heuristic to minimize the disparities of the class label
distribution with respect to the cluster memberships, with the assumption that
each cluster should ideally map to a distinct combination of attribute values.
Experiments demonstrate effective mitigation of cognitive biases on a benchmark
dataset without the use of annotations of secondary attribute values (the
zero-shot case) or with the use of a small number of attribute value
annotations (the few-shot case).
