Improving Learning-to-Defer Algorithms Through Fine-Tuning. (arXiv:2112.10768v1 [cs.LG])

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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