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Aurelius · Work & Leadership
Knowledge + Guidance

How do we own a decision the AI made but we can't explain?

Your team feeds numbers into a model, and it hands back a risk score none of you can fully trace. You act on it anyway. Then something goes wrong, and everyone looks at everyone else. This is not a broken tool. The model may be perfectly accurate. The real problem is that you are asked to answer for a choice you did not fully make, and cannot fully defend if questioned. You cannot force the model to explain itself to your satisfaction. You can force your team to say, out loud, what it actually knows and what it is simply trusting. That part is yours to control.

◆ How this problem reads on the two dials
GuidanceKnowledge
More coaching
A little to learn
1:1 with AureliusWith others (a Pod)
Some one-to-one
Practise with peers
The team needs a little teaching on where accountability actually lives, but mostly needs practice naming and owning decisions out loud.
How the two dials adapt to you →
What’s really going on

You do not need to trace every line the model drew to be accountable for the choice. You chose to use it. You chose to act on it. Name out loud what your team can verify and what it cannot, then own the decision anyway. That is the job.

🔒 What you’ll build togetherUnlock by starting
A moveBefore acting on the model's output, write one sentence: what we verified, what we are trusting.
A moveAgree who signs their name to the decision before anyone sees the number.
A moveWhen you present the output to others, say what you don't know first, not last.
A movePick one fact you already know to be true and check the model's output against it, every time.
A moveStop letting the model's confidence stand in for your own. If you are not sure, say so.
PractiseTrace and Own · a Pod of 4 · 30 min

What changes unlock by starting

  • Your team stops hiding behind 'the model said so.'
  • You can explain your decisions to others, even when you can't explain the machine.
  • Choices move faster because who owns the outcome is settled before the output arrives.
  • Trust inside your pod grows because no one pretends to know more than they do.
One object, two jobs: a public answer to a real problem, and — the moment you start the chat — Aurelius’s live plan for your version of it.