Algorithmic Bias & Fairness evaluates how a company identifies, mitigates and monitors unintended discriminatory outcomes in automated decision-making systems - particularly those involving artificial intelligence and machine learning. It covers:

• bias detection methodologies – statistical fairness audits, disparate-impact assessments, and subgroup performance metrics across race, gender, age, ability, geography and other protected or vulnerable characteristics;
• design and data practices – inclusive data collection, labelling and preprocessing to avoid historical bias, as well as transparency about data sources and limitations;
• fairness-enhancing interventions – algorithmic techniques (e.g., reweighing, adversarial de-biasing), human-in-the-loop review, and adjustments to decision thresholds or logic;
• governance and accountability – clear assignment of responsibility for fairness reviews, ethics approvals, and redress protocols in case of harm;
• public transparency – documentation of fairness metrics, limitations and trade-offs (e.g., in model cards or impact assessments) and engagement with stakeholders affected by automated decisions;
• alignment with best-practice frameworks including the EU AI Act, IEEE 7003, NIST AI RMF, and GRI/ESRS developments around algorithmic governance.