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arXiv · 2112.10768

Improving Learning-to-Defer Algorithms Through Fine-Tuning

Naveen Raman·Michael Yee
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Abstract

The ubiquity of AI leads to situations where humans and AI work together, creating the need for learning-to-defer algorithms that determine how to partition tasks between AI and humans. We work to improve learning-to-defer algorithms when paired with specific individuals by incorporating two fine-tuning algorithms and testing their efficacy using both synthetic and image datasets. We find that fine-tuning can pick up on simple human skill patterns, but struggles with nuance, and we suggest future work that uses robust semi-supervised to improve learning.

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BibTeXRIS

Naveen Raman, Michael Yee. 2021-12-18. Improving Learning-to-Defer Algorithms Through Fine-Tuning. https://arxiv.org/abs/2112.10768

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