  The analysis of discrimination has long interested economists and lawyers. In
recent years, the literature in computer science and machine learning has
become interested in the subject, offering an interesting re-reading of the
topic. These questions are the consequences of numerous criticisms of
algorithms used to translate texts or to identify people in images. With the
arrival of massive data, and the use of increasingly opaque algorithms, it is
not surprising to have discriminatory algorithms, because it has become easy to
have a proxy of a sensitive variable, by enriching the data indefinitely.
According to Kranzberg (1986), "technology is neither good nor bad, nor is it
neutral", and therefore, "machine learning won't give you anything like gender
neutrality `for free' that you didn't explicitely ask for", as claimed by
Kearns et a. (2019). In this article, we will come back to the general context,
for predictive models in classification. We will present the main concepts of
fairness, called group fairness, based on independence between the sensitive
variable and the prediction, possibly conditioned on this or that information.
We will finish by going further, by presenting the concepts of individual
fairness. Finally, we will see how to correct a potential discrimination, in
order to guarantee that a model is more ethical
