Fairness and Bias Mitigation constitute a core set of mandatory requirements under the EU AI Act, obliging providers of high-risk systems to proactively address risks of discrimination by employing appropriate data governance and technical measures throughout the system's lifecycle.

This obligation recognizes that AI systems can perpetuate, amplify, or introduce new forms of societal bias, leading to discriminatory outcomes that violate fundamental rights. The Act mandates a shift from passive non-discrimination to active bias mitigation. This involves a continuous process of identifying, evaluating, and minimizing risks of unfairness that could disadvantage individuals or groups based on protected characteristics such as sex, racial or ethnic origin, or disability. The focus is on ensuring equitable outcomes and treatment, requiring both technical solutions (like bias detection algorithms) and organizational processes (like diverse data auditing).

Effective bias mitigation under the Act requires a multi-faceted approach:

Data Governance & Representativeness: Implementing procedures to examine training, validation, and testing datasets for representativeness, identifying under-representation of groups, and assessing the presence of historical or societal biases embedded in the data.

Bias Detection & Testing: Utilizing statistical techniques and disaggregated evaluation to test the system's performance across different sub-populations, measuring disparities in error rates (e.g., false positives/negatives) that could indicate unfair treatment.

Bias Mitigation Strategies: Applying technical methods such as pre-processing (cleansing training data), in-processing (using fairness-aware algorithms), or post-processing (adjusting outputs) to reduce identified disparities, while carefully considering trade-offs with accuracy.

Impact Monitoring: Continuously monitoring the system's performance in the real world for signs of emergent discriminatory effects through post-market monitoring, and being prepared to retrain or adjust the model.

Regulatory Context: The requirement for fairness is embedded in multiple articles of the EU AI Act. Article 10 mandates that high-risk AI systems be developed with training, validation, and testing data that is "relevant, representative, free of errors and complete," with specific consideration given to "the persons or groups of persons on which the high-risk AI system is intended to be used." This is directly linked to fundamental rights protection.

Social License to Operate: Beyond legal compliance, rigorous bias mitigation is essential for obtaining and maintaining public trust. AI systems perceived as unfair face swift rejection, reputational damage, and legal challenges. Proactive fairness-by-design demonstrates a commitment to ethical AI and social responsibility, which is increasingly a market differentiator.