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AI Bias

Ορισμός

Systematic errors in AI systems that create unfair outcomes for certain groups, often arising from unrepresentative training data, flawed labelling, or problematic model design. Identifying and mitigating bias is a legal requirement for high-risk AI systems under the EU AI Act.

Σχετικοί Όροι

Algorithmic Bias

Systematic and repeatable errors in algorithmic outputs that create unfair advantages or disadvantages for individuals or groups based on protected characteristics. Algorithmic bias can arise from biased training data, proxy variables, or flawed evaluation metrics.

Fairness (AI)

The property of an AI system that ensures its outputs and decisions do not systematically disadvantage individuals on the basis of protected characteristics such as gender, ethnicity, or disability. Multiple mathematical definitions of fairness exist and may be in tension with one another.

AI Governance

The framework of policies, processes, roles, and controls that ensure AI systems are developed, deployed, and used responsibly and in compliance with applicable law. AI governance spans ethics, bias, privacy, security, and regulatory compliance across the model lifecycle.

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