Explainability, Interpretability, and Trustworthy AI
Why interpretability matters
Understanding predictions
Interpretability aims to explain why a model made a prediction, while trustworthiness includes robustness, calibration, safety, fairness, privacy, and accountability.
Explanation types
Global explanations
- Describe overall model behavior
- Useful for auditing and scientific insight
Local explanations
- Explain a specific prediction
- Useful for debugging and user-facing justification
Which is an example of a local explanation method?
Local explanations focus on why a specific input received a particular output.
Correct answer: Feature attribution for one instance
What does calibration measure?
A calibrated model’s confidence reflects empirical correctness rates.
Correct answer: How well predicted probabilities match observed frequencies.
Uncertainty and reliability
Predictive uncertainty can be epistemic, stemming from limited knowledge, or aleatoric, stemming from inherent noise. Reliable systems often combine uncertainty estimation with out-of-distribution detection and conservative decision rules.
Human factors
Interpretability is only useful if users can act on explanations; explanation quality should be evaluated in context, not assumed from mathematical elegance alone.
Which type of uncertainty is due to data noise rather than model ignorance?
Aleatoric uncertainty reflects irreducible randomness in the observations.
Correct answer: Aleatoric uncertainty
Trustworthy AI checklist
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1
Measure performance across relevant subgroups and shifts.
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2
Assess calibration and uncertainty quality.
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3
Test robustness under perturbation and distribution shift.
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4
Document limitations, assumptions, and failure modes.
Why is fairness tied to trustworthiness?
Fairness is a core component of accountable and socially robust AI systems.
Correct answer: Because reliable AI should avoid systematic harmful disparities across groups.