A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems

• URL: http://arxiv.org/abs/2407.12710v1
• Date: Wed, 17 Jul 2024 16:32:30 GMT
• Title: A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems
• Authors: Mohammad-Amin Charusaie, Samira Samadi,
• Abstract summary: Learn-to-Defer is a paradigm that enables learning algorithms to work not in isolation but as a team with human experts. In this paper, we obtain the Bayes optimal solution for learn-to-defer systems under various constraints. Our algorithm shows improvements in terms of constraint violation over a set of baselines.
• Score: 6.046591474843391
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learn-to-Defer is a paradigm that enables learning algorithms to work not in isolation but as a team with human experts. In this paradigm, we permit the system to defer a subset of its tasks to the expert. Although there are currently systems that follow this paradigm and are designed to optimize the accuracy of the final human-AI team, the general methodology for developing such systems under a set of constraints (e.g., algorithmic fairness, expert intervention budget, defer of anomaly, etc.) remains largely unexplored. In this paper, using a $d$-dimensional generalization to the fundamental lemma of Neyman and Pearson (d-GNP), we obtain the Bayes optimal solution for learn-to-defer systems under various constraints. Furthermore, we design a generalizable algorithm to estimate that solution and apply this algorithm to the COMPAS and ACSIncome datasets. Our algorithm shows improvements in terms of constraint violation over a set of baselines.

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