What is algorithmic bias? Definition and business implications

Algorithmic bias can emerge at three stages of the model's life cycle. First, in the training data: if the historical corpus reflects biased human decisions (past hiring, credit granting, case law), the model learns and reproduces those biases. Second, in the design choices: the chosen optimisation criterion (overall accuracy, for instance) can produce good average performance but errors concentrated on certain subgroups. Third, in the deployment context: a model trained on French 2018 data used on Belgian 2026 files will produce unexpected gaps. Algorithmic bias is not an exotic feature of AI models, it is their default condition. The question is not whether it exists, but which biases exist, at what scale, and with what operational consequences. Bias auditing is now a de facto obligation for any AI deployment with decisional impact on people.