STOA study on auditing the quality of datasets used in algorithmic decision-making systems

A recently published Panel for the Future of Science and Technology (STOA) study examines the impact of biases on datasets used to support decision-making systems based on artificial intelligence. It explores the ethical implications of the deployment of digital technologies in the context of proposed European Union legislation, such as the AI act, the data act and the data governance act; as well as the recently approved Digital Services Act and Digital Markets Act. It ends by setting out a range of policy options to mitigate the pernicious effects of biases in decision-making systems that rely on machine learning. Machine learning (ML) is a form of artificial intelligence (AI) in which computers develop their own decision-making processes for situations that cannot be directly and satisfactorily addressed by available algorithms. The process is adjusted through the exploration of existing data on previous similar situations that include the solutions found at the time. The broader and more balanced the dataset is, the better the chances will be of obtaining a valid result; but there is no a priori way of knowing whether the data available will suffice to collect all aspects of the problem at hand.