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.
@article{arxiv.2112.10768,
title = {Improving Learning-to-Defer Algorithms Through Fine-Tuning},
author = {Naveen Raman and Michael Yee},
journal= {arXiv preprint arXiv:2112.10768},
year = {2021}
}