Trading Efficiency for Control
The AI Conundrum in Migration Management
Bibliographic Data
| ID | 8239266 |
|---|---|
| Authors | Gianluca Iazzolino (0000-0003-4153-1521, corresponding author) |
| Year | 2025 |
| Volume | 17 |
| Issue | 1 |
| Pages | 35-46 |
| Publication date | 2025-03-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Cosmopolitan Civil Societies An Interdisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1837-5391 • E-ISSN: 1837-5391 |
| Publisher | University of Technology, Sydney (UTS) (PUBLISHER) |
| DOI | 10.5130/ccs.v17.i1.9423 |
| OpenAlex | W4408973083 |
| Language | EN |
| Citations received | 1 |
This paper contributes to the discussions on AI initiatives applied to migration management by drawing attention to critical issues in the AI systems field. It suggests a research agenda to investigate how AI-generated insights inform policies and how ideologies are reflected into policies and shape AI deployments. Specifically, this paper leverages the data justice and algorithmic accountability debates to examine two application of AI systems. The first, based on predictive AI, aims at supporting governments and humanitarian organisations in estimating timing, destination and size of refugee inflows. The second application refers to Natural Language Processing (NLP) and to the integration of voice and speech recognition within a broader repertoire of techniques to automate immigration systems. The paper finally suggests that to better harness the analytical power of AI, AI systems must be recognised as inherently political, in the sense that they enshrine a specific view of power and relations of subordination
Business · Control (management · Economics · Management · Operations management · Political science · Process management · Economic Policies and Impacts
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |