Impact of Responsibility Allocation Structures on Diagnostic Quality in AI-Assisted Diagnosis: Randomized Controlled Experiment
Tianya Liu, Ji Wu, Ivan Steenstra · 2026
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Description
Tianya Liu, Ji Wu, and Ivan Steenstra test, in 2026, how sharing blame with AI shapes doctors' judgement. Ninety-six doctors worked ten case puzzles in a mock clinic. Each group got a different rule for who answers for errors: the doctor fully, both equally, a shifting split, or no AI at all. Only the rule changed between groups.
The shifting split won clearly, lifting accuracy and trimming overconfidence. Full blame on the doctor added nothing over the control group. Equal sharing hinted at worse accuracy and clearly worse confidence balance. The authors say who carries the risk changes how closely doctors check AI advice. Design of blame rules is part of safe AI use.
Catalog record
| CREATOR | Tianya Liu, Ji Wu, Ivan Steenstra |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 26629 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |