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surveyChapter 3Leanpub · 2022

Interpretable Machine Learning: A Guide for Making Black Box Models Explainable

Christoph Molnar (LMU Munich)

Abstract

A comprehensive guide to interpretable machine learning, covering LIME, SHAP, and other methods for explaining black box model predictions. Essential reference for enterprise AI transparency.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

Interpretability is a governance requirement for Zone III in regulated industries. This guide provides the practical toolkit for making agent decisions explainable to stakeholders, auditors, and regulators.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Comprehensive interpretability survey
  • →LIME and SHAP explanations
  • →Enterprise AI transparency guide

Topics

interpretabilityexplainabilitySHAPLIME