Bias Detection

Bias detection encompasses the methods and tools used to identify, measure, and monitor unfair or discriminatory patterns in AI systems. While bias in AI is often discussed in terms of training data, effective bias detection must address multiple sources: historical biases embedded in datasets, measurement biases in how features are collected, algorithmic biases introduced by model design choices, and deployment biases that emerge when systems interact with real-world populations. Bias detection is not a one-time pre-launch activity but an ongoing operational requirement throughout an AI system's lifecycle.

The EU AI Act requires providers of high-risk AI systems to implement appropriate data governance practices, including examination of possible biases (Article 10). Risk management requirements (Article 9) mandate identifying risks related to the AI system's impact on fundamental rights, which directly encompasses discriminatory outcomes. For deployers using AI in contexts covered by the Fundamental Rights Impact Assessment (FRIA) requirements, bias monitoring becomes even more critical. Beyond regulatory compliance, bias in high-risk AI systems such as credit scoring, hiring, or insurance pricing can result in significant legal liability, reputational damage, and harm to affected individuals.

There are several types of bias to detect. Statistical bias occurs when a model's predictions systematically deviate from true values for certain groups, often measured through metrics like demographic parity or equalized odds. Representation bias emerges when training data underrepresents or misrepresents certain populations, leading to degraded performance for those groups. Measurement bias arises when the features or labels used to train a model capture different phenomena for different groups, such as using arrest records as a proxy for criminal behavior. Historical bias reflects societal inequities embedded in data that accurately represents a biased world, such as gender disparities in historical hiring decisions. Aggregation bias occurs when a single model is applied to groups with different underlying data distributions that would be better served by separate models.

Implement bias detection at multiple stages: during data preparation, model training, pre-deployment testing, and ongoing production monitoring. Define fairness metrics appropriate to your use case and the protected characteristics relevant to your jurisdiction. Document your bias testing methodology, results, and remediation actions as part of Annex IV technical documentation. Critically, distinguish between one-time testing and continuous monitoring; bias can emerge or shift as population distributions change over time. Establish thresholds that trigger review and remediation when bias metrics exceed acceptable limits.