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Unexploited Information Value in Human-AI Collaboration by Guo, Ziyang; Wu, Yifan; Hartline, Jason; Hullman, Jessica is a scholarly article available to read on EtoBox.

What is Unexploited Information Value in Human-AI Collaboration about?

Humans and AIs are often paired on decision tasks with the expectation of achieving complementary performance -- where the combination of human and AI outperforms either one alone. However, how to improve performance of a human-AI team is often not clear without knowing more about what particular information and strategies each agent employs. In this paper, we propose a model based in statistical decision theory to analyze human-AI collaboration from the perspective of what information could be used to improve a human or AI decision. We demonstrate our model on a deepfake detection task to investigate seven video-level features by their unexploited value of information. We compare the human alone, AI alone and human-AI team and offer insights on how the AI assistance impacts people's usage of the information and what information that the AI exploits well might be useful for improving human decisions.

Author

Guo, Ziyang; Wu, Yifan; Hartline, Jason; Hullman, Jessica

Published

2024

Language

EN