BeskrivelseAI is launched as – if not the panacea – then a very good remedy for making ends meet in the public sector, with demand for more services, fewer hands to do the job and more complex tasks. Several countries (e.g. New Zealand, China and Denmark) have experimented with using AI with varying degrees of success. Hence, the use of AI in the public sector has sparked both public and scholarly debate about the ethical considerations and the quality of data among other things. However there seems to be more talk than action.
The research community is eager to study the new phenomenon and during the last decade, several articles have been published, followed by a stream of literature reviews (see e.g. Bernd et al 2019; Weslei et al 2019). However, the review articles take a wide focus on the use of AI focusing on types of AI, sectorial differences and challenges with using AI. We need to know more about the extent to which AI influences the work of the frontline worker. Theories of Street-level bureaucracy, system level bureaucracy and streams within Science Technology Studies pose many assumptions (Lipsky 2010; Bovens & Zouridis 2005; Orlikowski 2000), but we lack empirical evidence. Hence, the aim of this study is to shed light on the existing knowledge about how AI has consequences for the street-level bureaucrat (SLB) in the public sector.
Based on a scoping literature review where 22 articles were included (out of 4277 articles), the paper contributes with insights into the consequences of AI for the frontline worker including the degree of discretion in decision making, the structure of their everyday work, and their relations to clients and managers. Additionally, we contribute to the somewhat still immature theoretical understanding of SLB’s interaction with technology- in this case AI.
|Periode||14 jun. 2022|
|Begivenhedstitel||4th Street-Level Bureaucracy conference|
|Grad af anerkendelse||International|
- machine learning