Data Mining and Automated Discrimination: A Mixed Legal/Technical Perspective

Socially sensitive decisions about critical issues such as employment, credit scoring, or insurance premiums are increasingly automated based on big data mining. Although algorithms do not have personal preferences, they are not neutral, and the data itself can reflect various undesirable biases. The authors discuss how discrimination-aware data mining constitutes a crucial step to counter automated discrimination. They then explain why the complexity of legal and social norms requires a balanced interdisciplinary methodology and toolset comprising requirements relating to data accuracy, protection, and provenance, and legitimacy of targeted objectives.