General AI

Human review and fairness

Reference only. Project progress and credentials come from assessed work.

Human review is useful only when the reviewer has enough information, time and authority to disagree.

Match review to consequence

  • Low-consequence, reversible drafts may need a quick shape check.
  • External claims need source and policy review.
  • Decisions affecting access, money, employment, safety or rights require a qualified owner and stronger evidence.

Review the system, not only one answer

A single good result does not prove that a workflow is fair or reliable. Review examples across languages, customer groups, uncommon cases and failure conditions.

Questions for the reviewer:

  1. Did the system use the permitted sources?
  2. Are important claims supported?
  3. Are uncertainty and missing information visible?
  4. Could the result disadvantage a person or group?
  5. Is the proposed action within policy and authority?

The reviewer must be able to reject the output without pressure to approve automation speed.

Human review and fairness | Mariete Academy