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Explainable AI needs formalization.

Stefan Haufe1,2,3, Rick Wilming3, Benedict Clark3

  • 1Technische Universität Berlin, Berlin, Germany.

NPJ Artificial Intelligence
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

Explainable artificial intelligence (XAI) methods often fail to provide accurate insights into machine learning models. Current XAI techniques require formal problem definition and targeted evaluation for reliable use in science and industry.

Keywords:
Computational scienceComputer science

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Last Updated: Apr 14, 2026

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05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Explainable artificial intelligence (XAI) aims to make machine learning (ML) decisions human-understandable.
  • Current XAI methods face scrutiny due to limitations in reliably answering questions about ML models, data, or inputs.

Purpose of the Study:

  • To critically evaluate the current state of XAI methods.
  • To identify fundamental reasons for the limitations of existing XAI techniques.
  • To propose a path forward for developing more reliable and validated XAI algorithms.

Main Methods:

  • Analysis of common XAI method shortcomings.
  • Identification of systematic errors in feature importance attribution.
  • Conceptual framework for defining explanation correctness.

Main Results:

  • Popular XAI methods incorrectly attribute importance to independent input features.
  • This inaccuracy limits XAI's utility for model diagnosis, scientific discovery, and intervention targeting.
  • Current XAI methods lack well-defined problems and objective evaluation criteria.

Conclusions:

  • XAI methods need formal problem definition and use-case-dependent evaluation criteria.
  • Developing objective metrics for explanation performance is crucial for validating XAI algorithms.
  • Future XAI research should focus on addressing well-defined problems with rigorous validation.