---
id: kb-2026-00277
title: Explainable AI (XAI)
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: "2026-05-22"
created_date: "2026-05-22"
generation_method: ai_structured
ai_models:
  - claude-opus
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-ai-001
    statement: Explainable AI research studies methods for making model behavior understandable to humans, especially for complex models whose internal reasoning is not directly interpretable.
    source_title: "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI"
    source_url: https://doi.org/10.1016/j.inffus.2019.12.012
    confidence: medium
  - id: fact-ai-002
    statement: LIME explains an individual model prediction by learning a local interpretable model around that prediction.
    source_title: "Why Should I Trust You? Explaining the Predictions of Any Classifier"
    source_url: https://arxiv.org/abs/1602.04938
    confidence: medium
  - id: fact-ai-003
    statement: SHAP uses Shapley-value-inspired additive feature attributions to explain model predictions.
    source_title: A Unified Approach to Interpreting Model Predictions
    source_url: https://arxiv.org/abs/1705.07874
    confidence: medium
completeness: 0.88
known_gaps:
  - This field is under active research and rapid development; some conclusions may evolve with new evidence or technological advances
  - Certain sub-topics are covered at a general level; specialized edge cases and nuanced applications may not be fully addressed
primary_sources:
  - title: "Why Should I Trust You? Explaining the Predictions of Any Classifier"
    type: academic_paper
    year: 2016
    authors:
      - Ribeiro, Marco Tulio
      - Singh, Sameer
      - Guestrin, Carlos
    institution: KDD
    url: https://arxiv.org/abs/1602.04938
  - title: A Unified Approach to Interpreting Model Predictions
    type: academic_paper
    year: 2017
    authors:
      - Lundberg, Scott M.
      - Lee, Su-In
    institution: NeurIPS
    url: https://arxiv.org/abs/1705.07874
  - title: "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI"
    type: academic_paper
    year: 2020
    authors:
      - Arrieta, Alejandro Barredo
      - Diaz-Rodriguez, Natalia
      - Del Ser, Javier
    institution: Information Fusion
    url: https://doi.org/10.1016/j.inffus.2019.12.012
secondary_sources: []
updated: "2026-05-24"
---

## TL;DR

Explainable AI (XAI) studies how to make model behavior understandable to humans. It is especially important for debugging, trust, governance, and high-stakes uses where a prediction alone is not enough.

## Core Explanation

Common post-hoc methods include LIME, which builds local interpretable approximations for individual predictions, and SHAP, which uses Shapley-value-inspired feature attributions. XAI methods can clarify model behavior, but explanations can still be incomplete or misleading when features are correlated, models are unstable, or the explanation method does not match the underlying decision process.

## Further Reading

- [Why Should I Trust You? Explaining the Predictions of Any Classifier](https://arxiv.org/abs/1602.04938)
- [A Unified Approach to Interpreting Model Predictions](https://arxiv.org/abs/1705.07874)
- [Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI](https://doi.org/10.1016/j.inffus.2019.12.012)