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Algorithmic Bias

Ορισμός

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.

Σχετικοί Όροι

AI Bias

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.

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.

Explainability (XAI)

The ability to understand and explain how an AI model reaches its decisions. Explainable AI is a mandatory requirement for high-risk AI systems under the EU AI Act and is critical for building trust with regulators, auditors, and end users.

Σχετικές Υπηρεσίες

Eu Ai Act Compliance

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