When developing AI systems, practitioners often focus on model building, while sometimes underestimating the importance of analysing the diverse data collection mechanisms. However, the diversity of mechanisms used for data collection deserves closer attention since each of them has different implications for AI developers, data subjects, and other rights holders whose data has been collected. This policy paper maps the principal mechanisms currently used to source data for training AI systems and proposes a taxonomy to support policy discussions around privacy, data governance, and responsible AI development.
