  Digital discrimination is a form of discrimination whereby users are
automatically treated unfairly, unethically or just differently based on their
personal data by a machine learning (ML) system. Examples of digital
discrimination include low-income neighbourhood's targeted with high-interest
loans or low credit scores, and women being undervalued by 21% in online
marketing. Recently, different techniques and tools have been proposed to
detect biases that may lead to digital discrimination. These tools often
require technical expertise to be executed and for their results to be
interpreted. To allow non-technical users to benefit from ML, simpler notions
and concepts to represent and reason about digital discrimination are needed.
In this paper, we use norms as an abstraction to represent different situations
that may lead to digital discrimination. In particular, we formalise
non-discrimination norms in the context of ML systems and propose an algorithm
to check whether ML systems violate these norms.
