AI Transparency, Explainability & Model Interpretation Playbook
- Practitioner
- Advanced
- Template Included
A framework for AI transparency, explainability, and model interpretation — selecting explainability approaches proportional to decision stakes, distinguishing genuine explanation from post-hoc rationalization, and building interpretation capability appropriate to different stakeholder audiences from technical teams to affected individuals.
Should organizations always choose the most explainable AI
approach available, even at some cost to accuracy? Not universally — the right explainability-accuracy trade-off depends on decision stakes; a low-stakes internal recommendation might reasonably prioritize accuracy, while a consequential decision affecting individuals (credit, employment) typically warrants prioritizing genuine explainability even at some accuracy cost.
What's the difference between genuine explanation and post-hoc
rationalization in AI interpretability? Genuine explanation reflects what actually drove the model's decision; post-hoc rationalization techniques can produce plausible-sounding explanations that don't accurately reflect the model's actual internal decision process — a distinction that matters significantly for high-stakes explainability requirements.
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