Summary of A Unifying Post-processing Framework For Multi-objective Learn-to-defer Problems, by Mohammad-amin Charusaie et al.
A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems
by Mohammad-Amin Charusaie, Samira Samadi
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to machine learning called Learn-to-Defer enables algorithms to collaborate with human experts, deferring some tasks to the expert while optimizing accuracy. The paper addresses the lack of methodology for developing such systems under constraints like algorithmic fairness and expert intervention budget. Using a d-GNP extension of the Neyman-Pearson lemma, the authors derive the Bayes optimal solution for learn-to-defer systems with various constraints. A generalizable algorithm is designed to estimate this solution and applied to COMPAS and ACSIncome datasets, showing improvements in constraint violation over baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Learn-to-Defer is a new way for computers and people to work together on tasks. Instead of doing everything alone, the computer can ask a person for help on some parts. This makes the team stronger and more accurate. But we don’t know how to make these teams work well when there are rules to follow. The paper finds a special formula that helps us make these teams better, even with rules like making sure everyone is treated fairly. They tested this idea on two big datasets and it worked really well! |
Keywords
» Artificial intelligence » Machine learning
