Over the years, lesson have been learnt from Dutch scandals involving risk profiling algorithms. Investigations conducted by consultants, academics and NGOs have contributed to a growing body of public knowledge from which best-practices emerge. This panel explores the interplay between the qualitative principles of law and ethics and the quantitative methodologies of statistics and data analytics. Specifically, we shed light on how empirical approaches can help interpret and contextualize open legal norms under EU non-discrimination law and public administration law. Examples are drawn from a recent audit conducted in collaboration with the Dutch Executive Agency for Education (DUO), in which aggregated statistics on the migration background of 300.000+ students were analyzed. We discuss whether bias testing inevitably leads to the feared ‘battle of numbers’, or whether it can serve as a critical role for fostering meaningful democratic oversight of AI.