Re-humanizing automated decision-making

(Academy of Finland 2020–2024)

The project examines how automated decision-making (ADM) relates to the infrastructures of our lives and shapes imaginaries of current and desired worlds. We began our work in 2020 by closely investigating a varied range of ADM cases in Finland, including credit scoring, predictive analytics for social care, chatbots and prisoners training AI. We treat these as exemplary cases that highlight current tensions and reveal actual and potential directions for future ADM developments. By the end of the project, we hope to have a better understanding of how to respond to transformations ushered in by ADM.

By focusing on ADM as a socio-technical process transforming everyday lives, we avoid connotations of machinic intelligence operating without human involvement. By re-humanizing ADM, we refer to the uncovering of human forces at play in automation. We argue that the imagined absence of humans limits our capacities to think of how ADM systems shape and transform everyday lives and societal structures. Re-humanizing ADM establishes the human as a critical and creative agent in human-machine relationships.

Our exemplary ADM cases trace practices of automation, explore how humans delegate responsibilities to machines, and examine how humans support or intervene in machinic decision-making. By doing so, they aid in unpacking dominant ADM imaginaries and in seeking their alternatives. With the aid of concrete cases, we are better equipped to study how ADM integrates into everyday practices and infrastructures, and to identify individual and societal implications of ADM systems. The final, integrative phase of the project concentrates on thinking creatively how to move beyond the dominant logics of automation.

The project collaborates closely with the non-profit research and advocacy organization AlgorithmWatch (see Automating Society reports 2019 and 2020), the ADM Nordic research network and the international Re-humanizing ADM research network.

Project leader:
Minna Ruckenstein