Bias in AI

Bias in AI refers to systematic patterns in an AI system that produce unfair, inaccurate, or discriminatory outcomes for certain groups. It is one of the most important challenges in responsible AI deployment.

Where bias comes from

AI bias has multiple sources:

• Training data bias — if the data used to train a model underrepresents certain groups, the model will perform poorly for those groups. A facial recognition system trained primarily on light-skinned faces will have higher error rates for darker-skinned faces.
• Historical bias — if the data reflects past discrimination, the model will learn and perpetuate it. A hiring model trained on a decade of hiring decisions will inherit any gender or racial bias present in those decisions.
• Selection bias — if the data collection process systematically excludes certain populations, the model cannot learn to serve them well.
• Measurement bias — if the features used to represent reality are imperfect proxies, they can introduce distortions.

Types of bias in practice

• Allocation bias — resources or opportunities distributed unequally (loan approvals, job recommendations)
• Representation bias — certain groups stereotyped or rendered invisible (image generation, search results)
• Quality of service — AI performs better for some groups than others (speech recognition accuracy across accents)

Mitigating bias

• Audit training data for representation gaps before training
• Test model performance across demographic groups, not just in aggregate
• Implement human review for high-stakes decisions
• Use diverse teams to identify blind spots in design and evaluation
• Document known limitations transparently

Bias is not just a technical problem

Bias mitigation requires organisational commitment, not just better algorithms. It involves decisions about what data to collect, whose feedback to prioritise, and what trade-offs are acceptable. These are business and ethical decisions, not purely engineering ones.