Every day you face the same decision dozens of times: the machine hands you an answer — do you trust it?

The research says we get this wrong in both directions. Decades of automation studies show that the more reliable a system becomes, the worse we supervise it — operators of highly reliable systems were about 50% less likely to catch failures. Meanwhile, Wharton's "algorithm aversion" experiments show we abandon algorithms after a single visible mistake, and in Stanford's diagnostic trials, doctors overruled correct AI answers so often that the AI alone beat doctors using the same AI.

Alex Rivers closes the opening Edge Igniter series with the judgement skill in daily practice: a three-question framework — What does it cost to be wrong? Is this situation normal? Can I check it? — that sorts every AI answer into accept, adjust, or re ...