治理

Algorithmic Bias

定义

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.

相关术语

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.

了解术语只是第一步，将其落地应用才是第二步。

预约一次探索通话，探讨这些 AI 概念如何转化到您所在的具体行业与业务挑战中。

Algorithmic Bias

定义

相关术语

AI Bias

Fairness (AI)

Explainability (XAI)

相关服务

了解术语只是第一步，将其落地应用才是第二步。

Algorithmic Bias

定义

相关术语

AI Bias

Fairness (AI)

Explainability (XAI)

相关服务

了解术语只是第一步，将其落地应用才是第二步。