NCA-AIIO · Question #62
Why is explainability critical in safety-critical AI systems?
The correct answer is C. It supports trust, accountability, and risk control. In domains like healthcare, aviation, autonomous vehicles, and finance, decisions made by AI have high-stakes consequences. Explainability allows human operators to understand why a model made a decision, verify that its reasoning is sound, catch errors or biases before they…
Question
Why is explainability critical in safety-critical AI systems?
Options
- AIt improves training speed
- BIt reduces model size
- CIt supports trust, accountability, and risk control
- DIt guarantees perfect accuracy
How the community answered
(47 responses)- A2% (1)
- B9% (4)
- C87% (41)
- D2% (1)
Explanation
In domains like healthcare, aviation, autonomous vehicles, and finance, decisions made by AI have high-stakes consequences. Explainability allows human operators to understand why a model made a decision, verify that its reasoning is sound, catch errors or biases before they cause harm, assign accountability when things go wrong, and satisfy regulatory requirements. These are the pillars of safe deployment. Explainability does not improve training speed (A) - it is a post-hoc or architectural concern. It does not reduce model size (B). And it absolutely does not guarantee perfect accuracy (D); models can be explainable yet still wrong.
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