The Map is a diagnostic. It tells you where you are most likely to be wrong before you read AI’s output, and after.

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Genesis

The biases themselves are old: Daniel Kahneman and Amos Tversky catalogued anchoring and availability in the 1970s; Herbert Simon named satisficing earlier still; automation bias is documented across aviation and medicine. What is new is the medium. AI is uniquely well-suited to amplifying each of the six because it produces fluent, polished, confident output at the speed of typing, and because it has been trained on what has already been done. The biases pre-exist; AI sharpens them.

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The six biases and their antidotes

Anchoring. Your prompt is the framing of the task, almost always a description of what you were already going to produce. Antidote: before you type, restate the problem twice from different vantage points.

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Availability. Easy-to-mind options feel more relevant; AI’s defaults reinforce the same set. Antidote: ask AI, ‘what approaches would a newcomer to my field, or an unrelated industry, try?’

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Satisficing. The first AI answer arrives in seconds and reads well. Antidote: by default, ask for two alternatives, one optimised for a different criterion and one written by an adversarial reader, and compare three.

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Automation. Polished output reads as authoritative; authority switches off scrutiny. Antidote: ask, ‘would I accept this from a colleague with this much working shown?’

Fluency. AI produces the prose of careful thought without the thought. Antidote: read slowly, stopping at each assertion: ‘what is the evidence for this?’ If the answer is ‘it sounds right’, keep looking.

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Confirmation. Ask for the case for restructuring and AI will produce it without flagging that it is delivering advocacy. Antidote: explicitly ask for the opposite case at equal length and rigour. Read them side by side.

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Why it matters now

AI does not weaken any of the six biases; it sharpens them. The Bias Map is what turns these defaults into something a leader can see, and therefore something a leader can interrupt. It is the diagnostic complement to the Gaussian Challenge (1.2): the biases are the reason most leaders never step outside the frame, and the Map is the audit you run on yourself to find out which one is biting hardest right now.

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How ChangeSchool applies it with executives

Two named exercises do most of the work in three-hour cohort sessions. The Prompt Audit preserves the unedited first prompt for a real task and reads it back as though a colleague had written it, surfacing decisions made before AI saw the work. The Output Audit prints the first AI output and audits it as a junior’s work: every number, every assertion, every load-bearing sentence held to evidence rather than fluency.

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Tested with chairs and governors in further education, deep-tech founders at the Royal Academy of Engineering, manufacturing leaders in the UK government’s Made Smarter programme, and C-suite cohorts in corporate populations.

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The discipline

Run one Prompt Audit a week and one Output Audit on every decision-driving AI output. Note which bias bit each time. The Map sharpens as the audits accrue: the bias your defaults reach for most often is the one to watch for first next time.

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“The Bias Map: six biases shape AI use, three in your prompt, three in your acceptance.”

 

Viren Lall, Managing Director,

ChangeSchool LDN (2026).

virenlall.com/bias-map

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