People need to understand why systems make decisions, not merely whether the systems appear to perform well. A map of the Explainable AI (xAI) landscape.

2025-11-28 · Source: Pascal’s Substack · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

The paper "Explaining explainability: A comprehensive survey on explainable artificial intelligence and relevant industry applications" maps the Explainable AI (xAI) landscape, asserting that explainability is crucial for trustworthy AI in high-stakes domains like healthcare, finance, and autonomous systems. It differentiates interpretability from explainability, noting that interpretable models are transparent by design while explainable models use post-hoc tools such as LIME, SHAP, and Grad-CAM. The survey's key contribution is a practical taxonomy that matches xAI methods to specific machine learning model types, user needs, and risk contexts. It also addresses the ongoing tension between model accuracy and transparency, the absence of robust general metrics for evaluating explanations, and the necessity of tailoring explanations for diverse audiences. The analysis further highlights xAI's inherent security and ethical risks, including data leakage and potential manipulation.

Key takeaway

For Directors of AI/ML evaluating new deployments, you must prioritize explainability as a core design requirement, not an afterthought. Unexplainable models, even if highly accurate, pose significant trust, compliance, and liability risks, potentially rendering them commercially unusable in regulated or reputation-sensitive environments. Ensure your teams integrate xAI throughout the model lifecycle and critically assess explanation faithfulness to avoid "xAI-washing."

Key insights

Explainability is now a prerequisite for trustworthy AI, demanding tailored methods for diverse contexts.

Principles

• Black-box AI is a growing practical, legal, and ethical problem.
• Explainability can be more critical than accuracy in high-risk domains.
• Post-hoc explanations risk creating a false sense of transparency.

Method

The paper proposes organizing xAI techniques by machine learning model type (supervised, unsupervised, reinforcement, computer vision, generative AI) to match explanation methods to the model, data, user, and risk context.

In practice

• Match xAI tools (e.g., Grad-CAM, SHAP) to model type and use case.
• Design explainability into the AI model lifecycle, not as an afterthought.

Topics

• Explainable AI
• AI Governance
• Model Interpretability
• Machine Learning Ethics
• Post-hoc Explanations
• AI Risk Management

Best for: CTO, VP of Engineering/Data, Executive, AI Scientist, AI Ethicist, Director of AI/ML

Related on AIssential

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• Explaining AI Without Code: A User Study on Explainable AI — Integrating XAI into no-code ML platforms improves usability and trust, but must balance novice accessibility with expert diagnostic depth.
• Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making — Integrating XAI with surrogate models enhances transparency and diagnostic capabilities for complex simulations, bridging efficiency and interpretability.
• We Need Explanation Cards to Connect Explanation Algorithms to the Real World — Explanation Cards bridge the gap between perceived and actual meaning of algorithmic explanations by providing crucial context and interpretation guidance.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Pascal’s Substack.