Which approach enables human oversight in critical AI decisions to prevent errors and bias?

Which approach enables human oversight in critical AI decisions to prevent errors and bias?

• A. Data quality in AI security
• B. Explainability in AI
• C. Human-in-the-loop in AI
• D. Chairperson

Answer: (C)

Integrating human-in-the-loop ensures human oversight in critical AI decisions by design, creating a validation step where a human can review, adjust, or veto the model’s output before action is taken. This is crucial for preventing errors and bias because it combines the efficiency of automation with the judgment, domain knowledge, and accountability of people. While improving data quality helps reduce bias at the input level and explainability aids understanding of how a decision was reached, neither guarantees active human review or intervention in real-time decisions. The chairperson option doesn’t address AI decision governance.

Integrating human-in-the-loop ensures human oversight in critical AI decisions by design, creating a validation step where a human can review, adjust, or veto the model’s output before action is taken. This is crucial for preventing errors and bias because it combines the efficiency of automation with the judgment, domain knowledge, and accountability of people. While improving data quality helps reduce bias at the input level and explainability aids understanding of how a decision was reached, neither guarantees active human review or intervention in real-time decisions. The chairperson option doesn’t address AI decision governance.