Multi-Objective Few-shot Learning for Fair Classification

• URL: http://arxiv.org/abs/2110.01951v1
• Date: Tue, 5 Oct 2021 11:28:58 GMT
• Title: Multi-Objective Few-shot Learning for Fair Classification
• Authors: Ishani Mondal, Procheta Sen, Debasis Ganguly
• Abstract summary: We propose a framework for mitigating the disparities of the predicted classes with respect to secondary attributes within the data. 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 to minimize the disparities of the class label distribution with respect to the cluster memberships.
• Score: 23.05869193599414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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).

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