Description

As explainability becomes a regulatory expectation and a trust requirement, organizations face a crowded and confusing market of tools claiming to make AI understandable. Selecting Explainability Tools provides a practical, buyer-focused guide for evaluating and adopting explainability solutions that align with real governance, risk, and operational needs.

This book is written for practitioners and leaders who must make informed purchasing and integration decisions without relying on marketing claims. It explains the different categories of explainability tools, what problems each is suited to address, and where common limitations and misconceptions arise.

Rather than promoting a single approach, the guide helps readers match tools to use cases, risk profiles, and stakeholder expectations. It also highlights integration considerations across data pipelines, model development workflows, and reporting processes.

Key topics include:

Categories of explainability and interpretability tools Feature and capability comparison criteria Evaluation checklists for technical and non-technical buyers Integration considerations for ML pipelines and governance workflows Common pitfalls, tradeoffs, and red flags in explainability tooling

Designed for technical managers and decision-makers, this book supports defensible, value-driven explainability investments that strengthen responsible AI practices.