Improving Learning-to-Defer Algorithms Through Fine-Tuning

• URL: http://arxiv.org/abs/2112.10768v1
• Date: Sat, 18 Dec 2021 19:57:16 GMT
• Title: Improving Learning-to-Defer Algorithms Through Fine-Tuning
• Authors: Naveen Raman, Michael Yee
• Abstract summary: We work to improve learning-to-defer algorithms when paired with specific individuals. We find that fine-tuning can pick up on simple human skill patterns, but struggles with nuance. We suggest future work that uses robust semi-supervised to improve learning.
• Score: 1.066048003460524
• License: http://creativecommons.org/licenses/by/4.0/
• 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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