Auditing the quality of datasets used in algorithmic decision-making systems

Biases are commonly considered one of the most detrimental effects of artificial intelligence (AI) use. The EU is therefore committed to reducing their incidence as much as possible. However, the existence of biases pre-dates the creation of AI tools. All human societies are biased - AI only reproduces what we are. Therefore, opposing this technology for this reason would simply hide discrimination and not prevent it. It is up to human supervision to use all available means - which are many - to mitigate its biases. It is likely that at some point in the future, recommendations made by an AI mechanism will contain less bias than those made by human beings. Unlike humans, AI can be reviewed and its flaws corrected on a consistent basis. Ultimately, AI could serve to build fairer, less biased societies. This study begins by providing an overview of biases in the context of artificial intelligence, and more specifically to machine-learning applications. The second part is devoted to the analysis of biases from a legal point of view. The analysis shows that shortcomings in this area call for the implementation of additional regulatory tools to adequately address the issue of bias. Finally, this study puts forward several policy options in response to the challenges identified.