AI Explainability in Practice

2024

Open publication workspace · Sign in to read the full PDF.

AI-generated summary

1) This workbook matters because it provides practical guidance and tools for implementing AI explainability, transparency, and accountability in public sector projects.
2)
- Defines key concepts, maxims, and main types of AI explanations alongside process-based and outcome-based approaches.
- Outlines an Explainability Assurance Management framework with six operational tasks and an accompanying template.
- Features a simulated case study on children's social care, interactive workshop activities, and appendices covering algorithmic techniques and supplementary models.

3) Tags: AI Explainability, Ethics and Governance, Transparency, Public Sector AI, Responsible AI

Check the original publication for accuracy and context.