The European Parliament’s Panel for the Future of Science and Technology (STOA) published a study on auditing the quality of datasets used in algorithmic decision-making systems (more specifically, in machine-learning applications). The study examines different types of bias, their causes, associated technical and legal complexities, and approaches to mitigating bias.
It puts forward a range of policy options, the most interesting of these are: (i) application and appropriate interpretation of existing and proposed draft legislation (e.g. non-discrimination laws, the General Data Protection Regulation, the Digital Services Act and the Digital Markets Act, the Data Governance Act, the Data Act, and the AI Act) – instead of adoption of new specific rules -- in order to tackle bias; and, (ii) introducing requirements and schemes for standardization and certification of datasets to be used in AI systems.
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