Give clinicians the tool's confidence and key reasoning so: Which design measure best achieves meaningful
Meaningful oversight requires giving the human the understanding and ability to effectively challenge and override outputs, not just to acknowledge them.
The question
A hospital deploys a high-risk AI triage tool. To satisfy the EU AI Act's human oversight requirement, the clinician must be able to catch and override incorrect recommendations rather than simply approve them by default. Which design measure best achieves meaningful oversight here?
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- Require clinicians to click an acknowledgment button confirming each recommendation before the patient case proceedsPlausible but weak: a mandatory click invites automation bias and rubber-stamping rather than genuine oversight.
- Give clinicians the tool's confidence and key reasoning so they can question and override a flawed recommendation ✓Correct: equipping the overseer to understand and effectively override the output delivers meaningful oversight.
- Log every recommendation the tool produces so the record can be reviewed during a later periodic auditLogging supports after-the-fact traceability, but it does not enable the clinician to intervene in real time.
- Publish the tool's overall accuracy metrics on the hospital's public website for patients to consultTransparency to the public is useful, yet it does not give the clinician the means to override a specific output.
The trap
Believing a mandatory acknowledgment click satisfies human oversight, when it actually encourages automation bias. How to remember it
Meaningful oversight requires giving the human the understanding and ability to effectively challenge and override outputs, not just to acknowledge them.
How many of these would you get right?
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