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GLOSSARY

What is Explainable AI?

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Quick Answer

Explainable AI (XAI) refers to artificial intelligence systems that provide human-understandable explanations for their decisions and predictions. Unlike black-box AI, explainable systems show their reasoning process, cite sources, and enable users to understand and trust AI recommendations.

Definition

**Explainable AI** is essential for regulated industries where decisions must be defended. XAI systems provide transparency into how conclusions are reached, which rules and data influenced decisions, and what alternatives were considered—enabling accountability and trust.

Key Points

  • Provides human-understandable explanations for AI decisions
  • Cites specific rules, policies, and data sources
  • Shows reasoning steps from input to output
  • Enables debugging and improvement of AI systems
  • Critical for regulatory compliance in healthcare, insurance, finance
  • Builds user trust and adoption

When NOT to Use This

  • For low-stakes decisions where transparency isn't required
  • When explainability would expose sensitive IP or competitive advantage
  • For purely internal AI tools not subject to external scrutiny

Frequently Asked Questions

Why is Explainable AI important for enterprises?

Regulated industries require defendable decisions. When regulators, customers, or auditors ask 'why did you decide that?', organizations need clear answers. Black-box AI creates liability and erodes trust.

Does explainability reduce AI accuracy?

Not necessarily. Modern XAI approaches like reasoning engines achieve high accuracy while maintaining transparency. The trade-off exists only for pure neural network approaches.

How detailed should AI explanations be?

Explanations should match the audience: business users need high-level rationale, compliance teams need policy citations, and technical teams need model details. Good XAI provides appropriate explanation depth.