A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems
<|reference_start|>A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems: 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.<|reference_end|>
@article{charusaie2024a,
    title={A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer
  Problems},
    author={Mohammad-Amin Charusaie, Samira Samadi},
    journal={arXiv preprint arXiv:2407.12710},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12710},
    primaryClass={cs.LG cs.AI}
}