Multi-Objective Few-shot Learning for Fair Classification
| Autor: | Procheta Sen, Debasis Ganguly, Ishani Mondal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Computation and Language Computer science business.industry Heuristic Small number Value (computer science) Function (mathematics) Machine learning computer.software_genre Class (biology) Cognitive bias Machine Learning (cs.LG) Benchmark (computing) Artificial intelligence Cluster analysis business computer Computation and Language (cs.CL) |
| Zdroj: | CIKM |
| Popis: | 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). Accepted as a short paper in CIKM 2021 |
| Databáze: | OpenAIRE |
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