Explainability, Robustness, and Ethical AI Engineering
Why Trustworthiness Matters
Beyond accuracy
A model can achieve high test accuracy and still be brittle, biased, or poorly calibrated. Advanced AI engineering requires explicit attention to interpretability, robustness under distribution shift, fairness, privacy, and accountability. TensorFlow workflows should therefore include evaluation and auditing beyond the main task metric.
Responsible AI principle
High performance is not sufficient if the model is unsafe, unfair, or unreliable in deployment.
What does calibration measure?
Calibration assesses the reliability of probability estimates.
Correct answer: Whether predicted probabilities match empirical correctness
Give one example of a robustness issue.
Small, carefully chosen changes to inputs can sometimes cause large prediction changes.
Correct answer: Sensitivity to adversarial perturbations
Interpretability Methods
Explaining predictions
Interpretability methods include saliency maps, integrated gradients, feature attribution, counterfactual examples, and concept-based analysis. These methods can reveal failure modes, but they are not perfect explanations. Advanced practice treats explanations as diagnostic tools that complement, rather than replace, rigorous evaluation.
Trustworthy AI toolkit
| Goal | Methods |
|---|---|
| Interpretability | Attributions, saliency, concept probes |
| Robustness | Adversarial testing, augmentation, randomized smoothing |
| Fairness | Group metrics, slice analysis |
| Calibration | Temperature scaling, reliability diagrams |
Why should explanations be treated carefully?
Many explanation methods are approximations and can be sensitive to settings and assumptions.
Correct answer: They can be approximate and method-dependent
Name one technique that can improve calibration.
Temperature scaling is a standard post-hoc calibration method.
Correct answer: Temperature scaling