Source: Universities – Science Po in English
Your work includes an analysis of algorithmic discrimination and of challenges in combatting it. What do you mean by this?
Algorithmic discrimination stems from a process that may seem simple at first glance, but that raises some thorny questions. Algorithms draw on vast quantities of data (so-called big data), from which they make recommendations, predictions, rankings and risk assessments, or provide answers to questions they are asked, among other things.
But data is obviously not neutral; it reflects existing discrimination and inequalities. Let’s take the case of hiring for a role in a traditionally male-dominated profession, such as information technology (IT). Analysis of existing data (from previous hiring, for example) would bring forth male applicants and might lead an algorithm trained on this data to favour male applicants in the future. Eliminating this bias is not impossible, but the process uncovers other biases, since the over-representation of men in IT results from their over-representation in this discipline in higher education, and it is difficult to disregard qualifications when hiring.
The cases are legion: by using statistical data and profiling based on gender, finances, addresses, and user health and age, some algorithms might block users’ access to a given good or service, or offer them worse conditions without any examination of their actual characteristics.
A decision not to use certain discriminating parameters generally requires the use of other parameters that appear neutral but are, in fact, strongly correlated with sensitive data. For example, even if a salary criterion is jettisoned to avoid socioeconomic discrimination, an address could provide the algorithm with indications of an individual’s social class. This phenomenon, known as redundant encoding, can create discrimination by proxy, that is, arising from data that is a priori non-discriminatory but that actually encodes certain inequalities.
Furthermore, bias affects not only the data, but also every stage in the deployment of an algorithm, from the formulation of the problem to be addressed to the interpretation of its results. These examples, and many others, show that eschewing bias in algorithms would require freeing society as a whole of bias.