Sometimes you make a good choice and it still goes wrong, just by bad luck. That doesn't mean it was a bad choice.
Outcome bias is when we judge a decision only by how it turned out. A sensible decision can end badly through bad luck, and a reckless one can get lucky. To learn anything, judge the decision by what was known at the time.
Outcome bias conflates decision quality with outcome quality. Counter it by recording information, options, probabilities and reasoning at decision time, then reviewing against that record rather than hindsight.
What it looks like
A project manager approves a launch after reasonable testing. A rare fault slips through and causes an outage. In the review, the decision is called "reckless". Six months earlier, a colleague skipped testing entirely, got lucky, and was praised for speed.
The same thing happens with AI. A model that's right 98% of the time will eventually make a visible mistake. If one bad case is enough to switch it off, you'll lose the value of the other 98%.
Why it matters
Outcome bias teaches the wrong lessons. People learn to avoid visible risks rather than make good decisions. They learn that luck counts as skill. Over time, the organisation becomes cautious about the wrong things.
How to counter it
Write down the decision before the outcome is known. Record the options, what you knew, what you expected and how confident you were. The decision record gives a format.
Review against the record, not the result. Ask: given what we knew, was this a reasonable choice? Was there information we should have had? Were our probabilities sensible?
Look at many decisions together. One outcome tells you little. Twenty decisions made the same way tell you a lot.