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Fairness (AI)

Ορισμός

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.

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.

AI Ethics

The application of moral principles—fairness, accountability, transparency, non-maleficence, and human dignity—to the design, development, and deployment of AI systems. AI ethics provides the normative foundation that regulations like the EU AI Act translate into enforceable obligations.

Accountability (AI)

The principle that individuals and organisations involved in AI development and deployment can be held responsible for the outcomes their systems produce. The EU AI Act establishes clear accountability chains between providers, importers, distributors, and deployers.

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