Bias risk arising from unrepresentative training data: Which AI | AIGP
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Bias risk arising from unrepresentative training data: Which AI risk are they describing?

AIGP Understanding the Foundations of AI Governance Medium

Unrepresentative training data causing systematic underservice of regions is bias risk.

The question

A logistics company deploys an AI routing system trained on historical data. Managers worry that unrepresentative training data could cause the system to systematically underserve certain regions. Which AI risk are they describing?

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  1. Misalignment risk arising from a reward function that optimizes a proxy goal diverging from the true business objective
    Plausible but wrong: the concern is skewed data producing unfair regional outcomes, not a mis-specified objective.
  2. Bias risk arising from unrepresentative training data that leads to systematically unfair outcomes for some regions ✓
    Correct: unrepresentative data causing systematic underservice of certain regions is bias risk.
  3. Security risk arising from an external adversary poisoning the model's inputs to manipulate its routing decisions
    Plausible but wrong: no adversary or poisoning is described; the issue is inherent data unrepresentativeness.
  4. Scalability risk arising from the system's inability to handle rising request volume as the whole company grows
    Plausible but wrong: the concern is fairness of outcomes across regions, not capacity under increasing load.
The trap
Reclassifying a data-representativeness fairness problem as a security or capacity issue.

How to remember it

Unrepresentative training data causing systematic underservice of regions is bias risk.

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