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AI Glossary

AI Bias

Systematic errors in AI outputs that result from biased training data or flawed model design.

TL;DR

  • —Systematic errors in AI outputs that result from biased training data or flawed model design.
  • —Understanding AI Bias is critical for effective AI for companies.
  • —Remova helps companies implement this technology safely.

In Depth

AI bias occurs when models produce outputs that systematically favor or disadvantage certain groups. Sources include biased training data, biased labeling, and algorithmic design choices. Enterprise AI governance must include bias detection and mitigation, particularly for decisions affecting people (hiring, lending, healthcare).

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.

Explainability (XAI)

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

AI Audit

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

Knowledge Hub

Glossary FAQs

AI Bias is a fundamental concept in the AI for companies landscape because it directly impacts how organizations manage systematic errors in ai outputs that result from biased training data or flawed model design.. Understanding this is crucial for maintaining AI security and compliance.
Remova's platform is built to natively manage and optimize AI Bias 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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