Bias & Fairness Assessment
AI systems can produce discriminatory outcomes — often unintentionally. The Bias & Fairness Assessment provides a structured methodology to evaluate your AI systems for bias risks, document affected protected attributes, and track mitigation measures.
What is a Bias & Fairness Assessment?
A bias and fairness assessment is a systematic evaluation of an AI system's potential to produce outcomes that unfairly disadvantage individuals or groups based on protected characteristics — such as race, gender, age, disability, ethnicity, or socioeconomic status.
Under the EU AI Act, providers of high-risk AI systems must examine training data for possible biases (Art. 10) and implement measures to prevent discriminatory outcomes. The EU Charter of Fundamental Rights (Art. 21) explicitly prohibits discrimination, and AI systems that score, rank, or make decisions about people are particularly exposed to this risk.
Why it matters
Legal obligation
The EU AI Act requires bias examination of training data (Art. 10(2)(f)) and non-discrimination safeguards for high-risk systems. Failure to comply carries fines up to 3% of global turnover.
Fundamental rights
AI-driven discrimination can violate the right to non-discrimination (EU Charter Art. 21), equal treatment directives, and national equality legislation.
Real-world impact
Biased AI in hiring, credit scoring, healthcare triage, or law enforcement has documented consequences — rejected applicants, denied loans, missed diagnoses, and wrongful arrests.
Reputational risk
Public disclosure of biased AI outcomes causes lasting reputational damage. Proactive assessment demonstrates responsible AI governance to regulators, customers, and the public.
Types of AI bias
Training data bias
Historical data reflects past discrimination. A hiring model trained on historical decisions inherits biases in who was previously hired or promoted.
Selection bias
The training dataset does not represent the population the system will serve. Underrepresented groups receive less accurate predictions.
Measurement bias
The features or labels used as proxies are themselves biased. Using zip codes as a feature can encode racial segregation patterns.
Aggregation bias
A single model is applied to groups with different underlying distributions. Medical AI trained primarily on one demographic may underperform for others.
Deployment bias
The system is used in a context different from the one it was designed for, or its outputs are interpreted in a biased manner by human operators.
What the template covers
Protected attributes
Identification of which protected characteristics (age, gender, race, disability, etc.) are relevant to the AI system's domain and affected population.
Data representativeness
Evaluation of whether training and testing data adequately represents all relevant groups, including historically underrepresented populations.
Fairness metrics
Selection and application of appropriate fairness metrics: demographic parity, equalized odds, predictive parity, or individual fairness measures.
Proxy detection
Analysis of whether ostensibly neutral features (location, language, education) serve as proxies for protected attributes.
Mitigation measures
Documentation of pre-processing, in-processing, or post-processing techniques used to reduce identified bias.
Monitoring plan
Ongoing monitoring strategy to detect bias drift after deployment, including thresholds and escalation procedures.
How it works
Create a new assessment
Select the Bias & Fairness template and link it to a registered AI system. The template structures the evaluation around the system's specific use case and affected population.
Evaluate each dimension
Work through the structured sections: identify relevant protected attributes, assess data representativeness, apply fairness metrics, and check for proxy variables.
Document findings and mitigations
Record identified bias risks with severity ratings. For each risk, document the mitigation approach — whether technical (algorithmic debiasing) or procedural (human review).
Review and monitor
Submit for approval, then establish ongoing monitoring. The assessment links to your AI system's record so bias findings inform risk classification and oversight decisions.