D!ARC Lecture: Automated and Datafied Welfare Futures
June 18th 2024 4.00 – 6.00 pm HS V.1.34
Dr. Doris Allhutter
Abstract:
In many countries across the globe, the public sector is expanding its efforts to introduce data-driven and intelligent systems in the administration of core welfare services such as social benefits provision, unemployment services and healthcare. Critical data studies and adjacent fields have raised concerns that datafication as a social and political process mainly caters to commercial and governance interests. The risks of automation in welfare have primarily been discussed as “black-boxing” (i.e. algorithmic assessments of welfare applicants rest widely inaccessible and unexplainable for case workers and citizens alike), as fundamentally problematic practices of profiling and categorizing citizens (which limits their life experience to what is observable and quantifiable, omitting the complexity of their life situations), and in terms of biased and discriminatory outcomes for protected groups in vulnerable life situations.
Linking research in STS, critical data studies, and social policy, my talk introduces an infrastructural approach to studying the performativity of emergent sociotechnical infrastructures of welfare. The increasing datafication and automation point to infrastructural transformations of welfare systems under austerity that rely on the segmentation of social protection through citizen profiling and the marketization/privatization of certain parts of social policy. I discuss this transformation by elaborating on 1) the intimate entanglement of computational profiling and segmentation of social policies along social categories such as gender, race, class, disability, and national descent and 2) the implicit values and operational logics introduced by the political economy of data-driven decision-making.
About:
Doris Allhutter is a senior scientist at the Institute of Technology Assessment (ITA), Austrian Academy of Sciences, where she leads the Austrian team of an international comparative study on Automating Welfare. Her research focuses on the implicit normativity of computing practices in machine learning under the lens of how these practices are entrenched in power relations. She is a Member of the UNESCO Advisory Board on Ethics of Artificial Intelligence and the current president of STS Austria. She was a visiting researcher at Vassar College New York (2019), UC Berkeley (2013), and Lancaster University (2011), as well as a Fellow at the Paderborn Research College ‘Data Society’ (2021), and the Digital Curation Institute, University of Toronto (2021/22).
