Algorithmic bias.

In computing, algorithmic bias occurs when algorithms repeatedly produce systematic errors, inaccurate results, or imbalanced outcomes as a result of prejudicial or unfair assumptions introduced at the programming level. It is a feature of machine learning (ML) and artificial intelligence (AI), which depend on large bodies of objective, reliable data. If the data used to create the algorithms that power ML and AI systems is flawed, discriminatory, or incomplete, the system will generate results that reflect these shortcomings.