Fairness and Bias are central Trustworthiness Characteristics in the NIST AI RMF that address the imperative for AI systems to produce equitable, just outcomes and to avoid creating, perpetuating, or amplifying unjust discrimination against individuals or groups, particularly those defined by protected characteristics.

This characteristic confronts the reality that AI systems can encode and scale historical and societal biases present in training data or introduced through design choices. "Fairness" is a multifaceted, context-dependent social and legal concept, while "bias" in this context refers to systematic statistical disparities that lead to unfair outcomes. The NIST AI RMF emphasizes a holistic approach that spans the entire AI lifecycle, from data collection and model development to deployment and monitoring, requiring both technical measures to detect and mitigate statistical disparities and socio-technical processes to define what constitutes a "fair" outcome in a given application.

Managing fairness and mitigating bias is a continuous process involving:

Bias Detection & Measurement: Using disaggregated evaluation to measure model performance (e.g., false positive/negative rates, predictive parity) across relevant subpopulations to identify significant disparities.

Data Provenance & Auditing: Scrutinizing training datasets for representativeness, historical biases, and the appropriateness of labels, and documenting these findings.

Algorithmic Fairness Techniques: Applying technical interventions such as pre-processing (adjusting training data), in-processing (using fairness-aware learning algorithms), or post-processing (adjusting model outputs) to reduce identified disparities.

Contextual Impact Assessment: Engaging with domain experts and stakeholders to understand the real-world meaning of statistical disparities and to define the appropriate fairness metric (e.g., equality of opportunity, demographic parity) for the specific use case.

Regulatory Context: This characteristic is directly legislated in the EU AI Act's requirements for data governance (Article 10), which mandates high-risk systems be trained on data that is "relevant, representative, free of errors and complete" with consideration for the persons on whom it is used. It underpins the Act's focus on protecting fundamental rights from algorithmic discrimination.

Social License and Legal Compliance: Proactive fairness management is essential for both ethical operation and legal risk mitigation. AI systems found to be discriminatory can lead to significant regulatory fines, litigation under civil rights laws, and severe reputational damage. A demonstrable commitment to fairness is increasingly a market differentiator and a requirement for doing business with public sector and conscientious private entities.