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 reject. Plus the aviation lesson of "children of the magenta line", why your GPS sounds equally confident driving you into a bay, and three habits that calibrate your trust over time, starting with an override log almost nobody keeps.

Chapters (approximate — verify against final audio)

00:00 — Intro 00:10 — The GPS that drove into the bay 01:30 — Failure one: automation bias (why reliable machines put us to sleep) 03:00 — Failure two: algorithm aversion (one strike and we're out) 04:15 — Why machines fail differently: confidence isn't competence 05:10 — The framework: three questions, ten seconds 07:00 — Accept, adjust, or reject: five worked examples 09:10 — Calibration habits: the override log, weird zones, flying manual 10:30 — Closing the series: the machine gets a vote, never the final vote 10:55 — This week's action 11:10 — How this episode was made (AI disclosure) and sign-off

Key takeaways

The most dangerous AI in your workplace is the excellent one nobody checks anymore — reliability breeds complacency, and training alone doesn't fix it. The skill isn't trusting AI more or less; it's calibration: matching scrutiny to stakes, normality, and verifiability, case by case. High stakes or abnormal situations shift the vote to the human; cheap-to-verify answers get verified before trusted. In safety-critical work, the machine can talk you into stopping, but should never talk you out of your own alarm. And calibration is a learnable skill: keep an override log, probe your tools' weak zones, and regularly work without the machine so the backup system — you — actually functions.