June 18^th 2024         04.00- 06.00 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.

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