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Algorithmic Bias: What It Is and How to Reduce It

•9 min read

Algorithmic bias is when an automated system produces results that are systematically unfair to certain groups of people. As businesses hand more decisions to software, from who to shortlist for a job to who gets offered credit, the risk is that a system quietly repeats or amplifies human prejudice at scale. This guide explains what algorithmic bias is, what causes it, the real cases that made it famous, and the practical steps a business can take to reduce it.

Bias in AI is not usually the result of anyone setting out to discriminate. It creeps in through the data and the design, which is what makes it dangerous: a system can look neutral and objective while producing unfair outcomes for thousands of people. Tackling it is now a core part of using AI responsibly, a theme our guide to AI ethics in business covers more broadly.

What algorithmic bias actually is

An algorithm is only as fair as the data and choices behind it. Algorithmic bias describes the situation where a model's outputs differ, in a way that is unjustified and harmful, according to characteristics such as sex, ethnicity, age or disability. The model has usually learned a pattern from the past and applied it to the future, so where the past was unfair, the prediction is too. Because the system is automated, that unfairness is applied consistently and at volume, which can make it worse than the scattered human bias it replaced.

What causes it

Bias enters AI systems through several routes, often more than one at once:

  • Biased training data. If historical data reflects human prejudice or under-represents a group, the model learns and repeats it.
  • The wrong target. Optimising for a convenient measure, such as past hiring decisions, can bake in the bias those decisions contained.
  • Proxy features. A feature like postcode or the name of a school can act as a stand-in for ethnicity or class, letting bias in through the back door.
  • Unrepresentative testing. A system tested only on a narrow group can perform far worse for everyone else without anyone noticing.

Real cases that made the risk clear

Several well-documented cases show how this plays out. In 2018 Amazon scrapped an experimental CV-screening tool after discovering it downgraded applications that included the word "women's", having learned from a male-dominated hiring history. Research such as the Gender Shades study found that commercial facial-recognition systems misclassified darker-skinned women far more often than lighter-skinned men. In the United States, investigative reporting on the COMPAS risk-scoring tool found it flagged some groups as higher risk more frequently. And the launch of the Apple Card in 2019 drew regulatory scrutiny after users reported very different credit limits for men and women. Different sectors, the same lesson: unexamined systems can discriminate.

How to reduce algorithmic bias

Bias cannot be switched off, but it can be managed like any other business risk. The practical steps that make the biggest difference are:

  • Use representative data. Check that the data reflects the people the system will affect, and correct gaps before training.
  • Test for fair outcomes. Measure results across groups before launch and monitor them afterwards, not just overall accuracy.
  • Keep a human accountable. A named person, not the algorithm, should be answerable for consequential decisions, with a route to challenge and override them.
  • Document the system. Record what it does, what data it uses and its known limits, so it can be reviewed and audited.
  • Run regular bias audits. Treat fairness testing as an ongoing task, because data and behaviour drift over time.

The rules are catching up

Algorithmic bias is increasingly a legal and compliance issue as well as an ethical one. In the UK, a system that discriminates on protected characteristics can breach the Equality Act 2010 and data-protection law. The EU AI Act places strict obligations on high-risk uses such as recruitment and credit scoring, and can apply to businesses outside the EU whose systems affect people inside it. Building fairness in from the start is far cheaper than retrofitting it after a complaint or an investigation. For the wider governance picture, see our guides to building an AI governance framework and to writing an AI use policy for employees, and to why this matters for business ethics as a whole.

Frequently asked questions

What is algorithmic bias?

Algorithmic bias is when an automated system produces results that are systematically unfair to particular groups of people. It usually happens not because anyone intended to discriminate, but because the data the system learned from reflected past bias, or the system was designed or measured in a way that disadvantages some people over others.

What causes algorithmic bias?

The most common cause is biased training data: if historical data carries human prejudice or under-represents a group, the model learns and repeats it. Other causes include choosing the wrong target to optimise, features that act as proxies for protected characteristics such as postcode standing in for ethnicity, and testing the system only on a narrow slice of users.

What is an example of algorithmic bias?

A well-known case is Amazon's experimental recruiting tool, scrapped in 2018 after it was found to downgrade CVs that mentioned women. Studies of facial recognition have also shown much higher error rates for darker-skinned women than for lighter-skinned men, and the US COMPAS risk tool was found to flag some groups as higher risk more often.

How can businesses reduce algorithmic bias?

Use representative data, test systems for unfair outcomes across groups before and after launch, keep a human accountable for important decisions, document how each system works, and run regular bias audits. Treating an AI system like any other high-impact process, owned, reviewed and improved, is the practical core of reducing bias.

Is algorithmic bias illegal?

It can be. In the UK, using a biased system that discriminates against people with protected characteristics can breach the Equality Act 2010 and data-protection law. Emerging rules such as the EU AI Act place strict obligations on high-risk uses like recruitment and credit, so bias is increasingly a legal and compliance risk, not just an ethical one.