ai_explainability_guidance_brief

2024

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AI-generated summary

1) One-sentence executive blurb (why it matters): This guidance brief provides practical methods and a governance framework to help AI project teams design, develop, and deploy transparent, explainable, and ethically sound artificial intelligence systems.
2) 3 bullets: what's inside:
- Defines core concepts such as AI explainability, transparency, and outcome- versus process-based explanations.
- Details six key types of explanations, including rationale, impact, fairness, safety, responsibility, and data explanations.
- Outlines a six-task Explainability Assurance Management approach integrated within the Process-Based Governance (PBG) Framework.
3) Tags: AI ethics, explainability, transparency, governance, machine learning

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