Which factor is central to ensuring AI fairness and accountability?

• A. Addressing AI bias and errors.
• B. Maximizing model size regardless of data quality.
• C. Ignoring stakeholder feedback.
• D. Relying solely on automated testing without human review.

Answer: (A)

Addressing AI bias and errors is central to ensuring AI fairness and accountability. When a system behaves unfairly or makes incorrect decisions, it undermines trust and raises questions about who is responsible for those outcomes. By actively identifying biases in data and model behavior, measuring disparate impact, and applying corrective steps, you create a foundation for fair treatment across groups and transparent, auditable decision processes. Error analysis and ongoing monitoring help you catch and fix mistakes, justify decisions, and demonstrate governance over the system’s use in the real world.

Why the other approaches don’t fit as the central factor: simply making the model larger without considering data quality can magnify hidden biases and reduce interpretability, not fairness. Ignoring stakeholder feedback leaves out crucial perspectives on real-world impact and accountability. Relying only on automated testing without human review misses nuanced, context-specific issues that humans are better suited to recognize and remediate.