Structuring the scattered literature on algorithmic profiling in the case of unemployment through a systematic literature review
Datos Bibliográficos
| ID | 11636018 |
|---|---|
| Autores | Kristian Bisgaard Haug (0000-0002-1509-675X, Roskilde University, autor de correspondencia) |
| Año | 2022 |
| Volumen | 43 |
| Número | 5/6 |
| Páginas | 454-472 |
| Fecha de publicación | 2022-07-09 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | International Journal of Sociology and Social Policy (JOURNAL) |
| Identificadores de la revista | ISSN: 0144-333X • E-ISSN: 1758-6720 |
| Editorial | Emerald Publishing Limited (PUBLISHER • GB) |
| DOI | 10.1108/ijssp-03-2022-0085 |
| OpenAlex | W4284961105 |
| Idioma | EN |
| Citas recibidas | 4 |
| Referencias citadas | 50 |
Purpose This article examines the overlooked literature on algorithmic profiling in public employment services (APPES) in the field of public administration. More specifically, it aims to provide an overview and connections to identify directions for future research. Design/methodology/approach To understand the existing literature, this article conducts the first systematic literature review on APPES. Through inductive coding of the identified studies, the analysis identifies concepts and themes, and the relationships among them. Findings The literature review shows that APPES constitutes an emerging field of research encompassed by four strands and associated research disciplines. Further, the data analysis identifies 23 second-order themes, five dimensions and ten interrelationships, thus suggesting that the practices and effects of algorithmic profiling are multidimensional and dynamic. Research limitations/implications The findings demonstrate the importance of future research on APPES undertaking a holistic approach. Studying certain dimensions and interrelationships in isolation risks overlooking mutually vital aspects, resulting in findings of limited relevance. A holistic approach entails considering both the technical and social effects of APPES. Originality/value This literature review contributes by connecting the existing literature across different research approaches and disciplines
Data science · Engineering ethics · Knowledge management · Management science · MEDLINE · Originality · Political science · Profiling (computer programming · Qualitative research · Social science · Sociology · Structuring · Systematic review · Computer Science · Digital Economy and Work Transformation · Engineering · Public Policy and Administration Research · Regulation and Compliance Studies
Artificial Intelligence and the Public Sector—Applications and Challenges
Statistical Modeling
The PRISMA 2020 statement
Seeking Qualitative Rigor in Inductive Research
A typology of reviews
Algorithmic Profiling of Job Seekers in Austria
AI governance in the public sector
Governing homo economicus
Employment services in an age of e-government
Artificial Discretion as a Tool of Governance
Implications of the use of artificial intelligence in public governance
How and where is artificial intelligence in the public sector going? A literature review and research agenda
Datafication and the Welfare State
Algorithmic Long-Term Unemployment Risk Assessment in Use
Transitions to long-term unemployment risk among young people
The usefulness of algorithmic models in policy making
Assessing the public policy-cycle framework in the age of artificial intelligence
Statistical Profiling of the Unemployed
Risk Technology in Australia
Unemployed citizen or ‘at risk’ client? Classification systems and employment services in Denmark and Australia
Feeling motivated yet? Long‐term unemployed people's perspectives on the implementation of workfare in Australia
From Street‐Level to System‐Level Bureaucracies
Street‐level bureaucrats, rule‐following and tenure
How governance conditions affect the individualization of active labour market services
Early reemployment for dislocated workers in the United States
Can Welfare Case Management Increase Employment? Evidence from a Pilot Program Evaluation
Administration by algorithm
Challenging algorithmic profiling
Targeted
Can Robots Understand Welfare? Exploring Machine Bureaucracies in Welfare-to-Work
Using Artificial Intelligence to classify Jobseekers
| Obras citantes distintas | 4 |
|---|---|
| Citas por año | 2 |
| Intervalo de citas | 2024 - 2025 (2) |
| Velocidad de citación | recent |
| Altamente citado | No |
| Tipos de cita | Neutras: 4 |