What is explainable AI and why is it important?

• A. AI whose decisions are opaque and cannot be explained.
• B. AI systems that reveal their data sources for every decision.
• C. AI whose decisions can be understood by humans, increasing trust and compliance.
• D. AI that avoids any human oversight.

Answer: (C)

Explainable AI is about making the reasons behind an AI decision understandable to people. This clarity matters because it builds trust, supports governance, and helps meet regulatory requirements, especially when decisions affect customers or risk. You can achieve it with models that are inherently interpretable or with techniques that reveal which factors influenced a decision and how they contributed. In practice, explainability lets everyone see that the system used appropriate inputs and logic—like which customer attributes swayed a service outcome—so audits, debugging, and fairness checks become possible.

The statement that best captures this is that AI decisions can be understood by humans, increasing trust and compliance. Opaque decisions undermine trust. Revealing data sources for every decision goes beyond explainability and can raise privacy concerns. And avoiding human oversight contradicts the purpose of explainability, which is to enable better governance and accountability.