What is AI Bias?

AI bias is not a hypothetical concern. Documented cases include lending algorithms that discriminated by postcode in ways that correlated with ethnicity, recruitment tools that penalised female candidates, and healthcare algorithms that underserved certain demographic groups. For UK regulated businesses, bias in AI systems creates risk under the Equality Act 2010, sector-specific regulations, and the reputational consequences of unfair treatment.

Bias enters AI systems through multiple routes. Historical bias occurs when training data reflects past discrimination, such as lending decisions that were themselves biased. Representation bias occurs when certain groups are underrepresented in training data, causing the model to perform poorly for those groups. Measurement bias occurs when the features used as inputs are themselves imperfect proxies that correlate with protected characteristics. Selection bias occurs when the data collection process systematically excludes certain populations.

For financial services firms, the FCA expects that AI systems used for customer-affecting decisions do not produce systematically worse outcomes for particular groups. This means testing AI outputs across demographic segments, monitoring for differential impact, and having processes to investigate and remediate bias when it is detected. The Consumer Duty reinforces this by requiring firms to consider the needs of vulnerable customers and those with protected characteristics.

In healthcare, bias can directly affect patient safety. An AI triage system that performs less accurately for certain demographic groups could delay treatment for those patients. Clinical AI systems need validation across the full range of patient populations they will serve.

Mid-market firms can address AI bias through practical steps. Start by understanding the composition of your training data and identifying potential gaps or historical biases. Test AI outputs across different demographic segments before deployment. Monitor live systems for differential outcomes on an ongoing basis. Maintain a clear process for investigating and responding to bias concerns. And recognise that bias mitigation is an ongoing process, not a one-time check, as both data distributions and societal expectations evolve over time.