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AI Glossary

Explainability (XAI)

The ability to understand and explain how an AI model arrives at its outputs or decisions.

TL;DR

  • —The ability to understand and explain how an AI model arrives at its outputs or decisions.
  • —Understanding Explainability (XAI) is critical for effective AI for companies.
  • —Remova helps companies implement this technology safely.

In Depth

Explainable AI (XAI) provides transparency into model decision-making. In enterprise settings, explainability is important for regulatory compliance (especially in finance and healthcare), building user trust, debugging model behavior, and satisfying audit requirements.

Related Terms

AI Ethics

The principles and guidelines governing the responsible development and use of AI systems.

Responsible AI

An approach to AI development and deployment that prioritizes safety, fairness, transparency, and accountability.

AI Audit

A systematic examination of AI system operations, decisions, and impacts for compliance and quality assurance.

AI Transparency

The practice of being open about how AI systems work, what data they use, and how decisions are made.

Knowledge Hub

Glossary FAQs

Explainability (XAI) is a fundamental concept in the AI for companies landscape because it directly impacts how organizations manage the ability to understand and explain how an ai model arrives at its outputs or decisions.. Understanding this is crucial for maintaining AI security and compliance.
Remova's platform is built to natively manage and optimize Explainability (XAI) through our integrated governance layer, ensuring that your organization benefits from this technology while mitigating its inherent risks.
You can explore our full AI for companies glossary, which includes detailed definitions for related concepts like AI Ethics and Responsible AI.

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