REVIEW 1 cited by
Monitoring Human Dependence On AI Systems With Reliance Drills
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
AI systems are assisting humans with increasingly diverse intellectual tasks but are still prone to mistakes. Humans are over-reliant on this assistance if they trust AI-generated advice, even though they would make a better decision on their own. To identify such instances of over-reliance, this paper proposes the reliance drill: an exercise that tests whether a human can recognise mistakes in AI-generated advice. Our paper examines the reasons why an organisation might choose to implement reliance drills and the doubts they may have about doing so. As an example, we consider the benefits and risks that could arise when using these drills to detect over-reliance on AI in healthcare professionals. We conclude by arguing that reliance drills should become a standard risk management practice for ensuring humans remain appropriately involved in the oversight of AI-assisted decisions.
Forward citations
Cited by 1 Pith paper
-
When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration
Model benchmark performance only weakly predicts how well people learn from AI explanations, with notable outliers across code and math.
Discussion (0). Continue with ORCID to comment.