What is the importance of explainability in AI?

• A. To ensure users understand AI outputs and maintain trust
• B. To increase model complexity
• C. To reduce transparency
• D. To obscure decision criteria

Answer: (A)

Explainability in AI means making the reasons behind a model’s outputs understandable to humans so people can interpret and evaluate what the AI is doing. This matters because when users can see why a prediction or recommendation was made, they can assess its reliability, check for biases, and determine whether it aligns with goals, risk tolerances, and any regulatory or governance requirements. Keeping outputs understandable helps build trust and supports accountability and responsible use. Conversely, adding complexity without visibility or deliberately obscuring how decisions are made would undermine trust, make validation harder, and raise concerns about fairness and safety.