How to make the most of machines?
A recording of the talk can be viewed below:
The event is hybrid, with the talk happening in the Computer Science Building (Room A133 T5, Konemiehentie 2) at Aalto University, and on Zoom (link will be provided later).
Humans and machines are inseparable in the ’Real AI for Real People in the Real World’, but we tend to pay too little attention to the work that humans do for the machines. In order to build societally robust algorithmic systems, the maintenance and repair work that such systems require needs to be acknowledged, alongside appropriate divisions of labor between humans and machines. Ideally, algorithmic systems combine the strengths of machines with those of humans. In order to get there, however, we have to learn what those strengths are, in particular situations, and how to make the most of them.
This talk outlines a research approach that aims to re-humanize algorithmic systems. Such re-humanizing is not a new endeavor, but it is needed with ever-increasing urgency. With the spread of algorithmic systems from media and health to urban planning and education, algorithms have drifted out of the computational realm to shape the everyday. Credit scoring, hiring practices, allocation of social benefits, health care diagnostics, and student evaluations can now rely on algorithmic logics. The focus on re-humanizing emphasizes that we need to stay close to how algorithmic systems shape and seek to transform everyday lives and societal structures. Methodological choices are at the core of re-humanizing efforts, as they will influence how we think about present and future possibilities in relation to technologies. When people play the leading role in our inquiry, we can trace their influences, explore their practices, and eventually sketch alternative futures from the ground up.
Minna Ruckenstein works as professor at the Consumer Society Research Centre. She directs The Datafied Life Collaboratory that studies processes of digitalization/datafication by highlighting emotional, social, political and economic aspects of current and emerging data practices. The disciplinary underpinnings of the work range from anthropology of technology, science and technology studies and communication to consumer economics.
Currently funded research projects focus on re-humanizing automated decision-making, algorithmic culture, and everyday engagements with algorithmic systems in Helsinki and in Shanghai and Hangzhou. Ongoing cases studies mainly deal with everyday and organizational aspects of datafication, in fields ranging from content moderation and advertising to digital health, insurance, and social work.