Trust Explanations to Do What They Say
February 14, 2023 Β· Declared Dead Β· π arXiv.org
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Authors
Neil Natarajan, Reuben Binns, Jun Zhao, Nigel Shadbolt
arXiv ID
2303.13526
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
How much are we to trust a decision made by an AI algorithm? Trusting an algorithm without cause may lead to abuse, and mistrusting it may similarly lead to disuse. Trust in an AI is only desirable if it is warranted; thus, calibrating trust is critical to ensuring appropriate use. In the name of calibrating trust appropriately, AI developers should provide contracts specifying use cases in which an algorithm can and cannot be trusted. Automated explanation of AI outputs is often touted as a method by which trust can be built in the algorithm. However, automated explanations arise from algorithms themselves, so trust in these explanations is similarly only desirable if it is warranted. Developers of algorithms explaining AI outputs (xAI algorithms) should provide similar contracts, which should specify use cases in which an explanation can and cannot be trusted.
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