Which term describes the difficulty of explaining AI decisions to stakeholders due to opacity?

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Multiple Choice

Which term describes the difficulty of explaining AI decisions to stakeholders due to opacity?

Explanation:
Explaining AI decisions to stakeholders becomes difficult when models are opaque, lacking transparency and interpretability. Opacity comes from complex algorithms and learned representations that make it hard to trace how a given input leads to a particular outcome. Transparency means we can clearly inspect the model’s structure and factors it uses, while interpretability means humans can understand why a decision was made, or at least see a relatable justification. When both are missing, communicating the rationale, validating outcomes, and addressing concerns like bias or risk becomes challenging, which is exactly what stakeholders need for trust and governance. The other terms refer to data handling topics—where data came from, how data is categorized, or managing metadata—not to how explainable the model’s decisions are, so they don’t capture this explanatory gap.

Explaining AI decisions to stakeholders becomes difficult when models are opaque, lacking transparency and interpretability. Opacity comes from complex algorithms and learned representations that make it hard to trace how a given input leads to a particular outcome. Transparency means we can clearly inspect the model’s structure and factors it uses, while interpretability means humans can understand why a decision was made, or at least see a relatable justification. When both are missing, communicating the rationale, validating outcomes, and addressing concerns like bias or risk becomes challenging, which is exactly what stakeholders need for trust and governance. The other terms refer to data handling topics—where data came from, how data is categorized, or managing metadata—not to how explainable the model’s decisions are, so they don’t capture this explanatory gap.

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