A human-in-the-loop approach to AI and Machine Learning places qualified people inside the decision workflow to review evidence, apply context, correct errors, and take responsibility for action.
Human review should be designed, not added as a vague final step. The reviewer needs enough evidence, authority, time, and training to challenge or override the model.
The level of oversight should reflect the consequence of a wrong result, the maturity of the use case, and how well performance is measured under real operating conditions.
