|Understanding algorithmic decision-making: Opportunities and challenges|
While algorithms are hardly a recent invention, they are nevertheless increasingly involved in systems used to support decision-making. These systems, known as 'ADS' (algorithmic decision systems), often rely on the analysis of large amounts of personal data to infer correlations or, more generally, to derive information deemed useful to make decisions. Human intervention in the decision-making may vary, and may even be completely out of the loop in entirely automated systems. In many situations, the impact of the decision on people can be significant, such as access to credit, employment, medical treatment, or judicial sentences, among other things. Entrusting ADS to make or to influence such decisions raises a variety of ethical, political, legal, or technical issues, where great care must be taken to analyse and address them correctly. If they are neglected, the expected benefits of these systems may be negated by a variety of different risks for individuals (discrimination, unfair practices, loss of autonomy, etc.), the economy (unfair practices, limited access to markets, etc.), and society as a whole (manipulation, threat to democracy, etc.). This study reviews the opportunities and risks related to the use of ADS. It presents policy options to reduce the risks and explain their limitations. We sketch some options to overcome these limitations to be able to benefit from the tremendous possibilities of ADS while limiting the risks related to their use. Beyond providing an up-to-date and systematic review of the situation, the study gives a precise definition of a number of key terms and an analysis of their differences to help clarify the debate. The main focus of the study is the technical aspects of ADS. However, to broaden the discussion, other legal, ethical and social dimensions are considered.