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Cited by 141Migration, Refugees, and IntegrationGender and Women's RightsEthics and Social Impacts of AI

The DOI matches its Crossref record; 71 of the 77 references it cites were found in a registry or an index.

International migration management in the age of artificial intelligence

Open access
Beduschi, Ana·2021·Migration Studies
DOI 10.1093/migration/mnaa003
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Abstract

Abstract Artificial intelligence (AI) has the potential to revolutionise the way states and international organisations seek to manage international migration. AI is gradually going to be used to perform tasks, including identity checks, border security and control, and analysis of data about visa and asylum applicants. To an extent, this is already a reality in some countries such as Canada, which uses algorithmic decision-making in immigration and asylum determination, and Germany, which has piloted projects using technologies such as face and dialect recognition for decision-making in asylum determination processes. The article’s central hypothesis is that AI technology can affect international migration management in three different dimensions: (1) by deepening the existing asymmetries between states on the international plane; (2) by modernising states’ and international organisations’ traditional practices; and (3) by reinforcing the contemporary calls for more evidence-based migration management and border security. The article examines each of these three hypotheses and reflects on the main challenges of using AI solutions for international migration management. It draws on legal, political and technology-facing academic literature, examining the current trends in technological developments and investigating the consequences that these can have for international migration. Most particularly, the article contributes to the current debate about the future of international migration management, informing policymakers in this area of growing importance and fast development.

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  1. Backer, 2018, Next Generation Law: Data-Driven Governance and Accountability Based Regulatory Systems in the West, and Social Credit Regimes in China, Southern California Interdisciplinary Law Journal, 28, 123
  2. Carammia, 2018, Can We Anticipate Future Migration Flows?
  3. Chui, 2018, Notes from the AI Frontier. Applying Artificial Intelligence for Social Good
  4. Connor, 2017, The Digital Footprint of Europe’s Refugees
  5. Eubanks, 2018, Automating Inequality
  6. Gelb, 2018, Technology, Migration and the 2030 Agenda for Sustainable Development
  7. 2017, Data Bulletin: Informing a Global Compact for Migration. Issue No 3
  8. 2018, Big Data and Migration
  9. 2019, Migration Data Portal
  10. 2019, China
  11. Moor, 2006, The Dartmouth College Artificial Intelligence Conference: The Next Fifty Years, AI Magazine AAAI, 27, 87
  12. 2017, The Digital Footprint of Europe’s Refugees
  13. Rango, 2015, How Big Data Can Help Migrants
  14. Richardson, 2019, Dirty Data Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice, New York University Law Review, 94, 192
  15. Tangermann, 2017, Documenting and Establishing Identity in the Migration Process. Challenges and Practices in the German Context
  16. 2012, Demystifying Big Data: A Practical Guide to Transforming the Business of Government
  17. 2018, Biometrics in the Humanitarian Sector
  18. Wachter, 2018, Counterafctual Explanations without Opening the Black Box: Automated Decisions and the GDPR, Harvard Journal of Law & Technology, 31, 842
  19. 2013, Human Rights Impact Assessments. A Review of the Literature, Differences with Other Forms of Assessments and Relevance for Development
  20. 2018, Technology Landscape for Digital Identification

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Metadata

Authors
Beduschi, A.
Affiliations
University of Exeter (GB)
Corresponding author
Beduschi, Ana
ORCID
Beduschi, Ana
Venue
Migration Studies
Publisher
Oxford University Press (OUP)
Published
2020-02-10
Volume
9
Issue
3
Pages
576-596
ISSN
  • 2049-5838 (ISSN-L)
  • 2049-5846
DOI
10.1093/migration/mnaa003
OpenAlex
W3005767652
Cited by
141
References
77
Citations by year
2026: 32 · 2025: 31 · 2024: 20 · 2023: 24 · 2022: 17 · 2021: 13 · 2020: 4
FWCI
19.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Migration Studies (publishedVersion, cc-by)
  • Open Research Exeter (University of Exeter) (publishedVersion, cc-by)
  • SSRN Electronic Journal (submittedVersion)
  • Figshare (submittedVersion, cc-by)
Topics
  • Social Sciences › Sociology and Political Science › Migration, Refugees, and Integration
  • Social Sciences › Political Science and International Relations › Gender and Women's Rights
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Keywords
artificial intelligence, international migration management, border security, asylum determination, algorithmic decision-making, identity verification, international organizations, face recognition, dialect identification

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