Reducing Algorithmic Bias

Algorithmic bias is not the computer being deliberately unfair, and it is not inevitable: it comes from the data the system learned from and from the choices made when it was designed and used. Because those causes are human ones, they can be found and reduced, even if bias is rarely removed completely.

More representative training data

The strongest fix is usually the data. A machine learning system copies the patterns it is trained on, so if a group is missing or rare in the training data, the system will make weaker decisions for that group. Collecting representative training data — data that reflects the real mix of people or cases the system will meet — gives the model a fairer picture to learn from.

For example, a leisure centre uses an AI to predict which members are likely to cancel, so staff can offer help. If the records come almost only from adult members, the system barely learns about teenage members and flags them at random. Adding records from every age group, and checking that past decisions in those records were fair, makes the predictions far more balanced.

Testing outputs and auditing

Testing outputs means checking results for different groups rather than only the overall accuracy. A system that is 90% accurate overall could still be wrong far more often for one group, and only group-by-group testing shows that. Auditing goes further: an independent review of the system and the patterns in its decisions over time, including whether biased decisions feed back as new data and make the bias stronger.

People around the system

Technical fixes alone are not enough. Human oversight means a person reviews decisions that really matter, so an unfair pattern is caught before it does harm. Transparency means people can see how decisions are reached, and governance means clear rules and a named person responsible for checking fairness. Involving the people affected matters too, because they notice unfairness that the data does not show, such as wheelchair users testing a hotel booking system that keeps failing to offer accessible rooms.