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Majority label bias

The tendency of an AI to over-predict whichever label appears most often in its examples.

Say it to an AI

Label each ticket High, Medium or Low. High = many people blocked now; Medium = one team degraded; Low = requests and minor issues. Examples: “Whole office is offline” → High; “One shared file won't open” → Medium; “Request: new mouse” → Low. One line per ticket.

How an expert would ask for the stronger version of “IT ticket triage”.

See it

Try it as a puzzle →

An office IT admin asks an AI to set a priority on this morning's helpdesk tickets.

Study A · the weaker one
Study B · the stronger one

Why: A's examples are 3 × High, and the reply skews every new ticket upward: 'out of toner' → High, 'a second monitor' → Medium.

Related words

  • Plain language
  • Tone
  • Output format
  • Success criteria
  • Context
  • Grounding
  • Constraints
  • Hallucination

Room 4 · Examples & references

Learn “Majority label bias” in a five-minute lesson

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