Practical Considerations

Chapter 24: Human-Centric Explanations Designing explanations for diverse stakeholders [Interactive dashboards, explanation tuning, user studies] Chapter 25: Fairness and Ethics in Interpretability Addressing bias and ethical challenges in explanations [Fairness-aware SHAP, bias auditing, explanation fairness metrics] Chapter 26: Causal Interpretability Causal methods for understanding model decisions [Causal tracing, interventional explanations, causal effect variate] Chapter 27: Real-Time Interpretability Generating explanations for dynamic and interactive systems [Online SHAP, incremental counterfactuals, streaming explanations] Chapter 28: Evaluation of Interpretability Methods Metrics and challenges in assessing explanations [Fidelity, simplicity, user trust, human-in-the-loop evaluation] Chapter 29: Interpretability Benchmarks and Datasets Standardized frameworks for comparing and evaluating explanations [InterpretML, SHAP benchmarks, synthetic datasets, real-world test cases] Chapter 30: Robustness of Explanations Ensuring explanations are reliable under noise or attacks [Robust SHAP, certified interpretability, stability metrics, adversarial explanation defense]