Although data systems are rapidly rolled out in the public sector and state-citizen relations become increasingly automated, there is little public knowledge about these developments, with few possibilities to intervene into, and participate in, decisions about the use of data systems. How, then, do we advance people’s voice in the deployment of data and AI? How do we democratise datafied governance?

Things can go disastrously wrong on data science and machine learning projects when we undervalue data work, use data in contexts that it wasn’t gathered for, or ignore the crucial role that humans play in the data science pipeline.

The interpretation process can be detached from the model architecture

As AI systems are being leveraged and scaled, frequently calls are made for, “meaningful human control” or “meaningful human interaction on the loop.” Originally an engineering term, requiring a human “in the loop” who could halt systems, the term “on the loop” or “nearby the loop” has been co-opted by policymakers in order to distance humans further from systems.