Technology, Displaced? The Risks and Potential of Artificial Intelligence for Fair, Effective, and Efficient Refugee Status Determination
Human vulnerability is at the core of refugee status determination and human rights provides its regulatory frame, so to speak of artificial intelligence within the refugee context may seem troubling at least, dystopian at worst. But the rapid development of artificial intelligence in government decision-making will unlikely be slowed by such ethical quandaries. The potential integration of automation, machine learning and algorithmic decision-making into global migration regulation and policy has far-reaching implications for refugee law. The consequences for efficiency, legality, accountability, transparency and human rights warrant a timely and critical conversation about the possible impact of existing and future technologies on refugee status determination. Predictive analytics, biometrics, automated credibility assessments and algorithmic decision-making are technologies that could have utility for refugee status determination processing, credibility assessments and decision-making. Each technology is considered through a lens of ‘risk and potential’, which is measured in terms of ‘fair, efficient and effective’ refugee status determination. The opportunities that artificial intelligence offers for efficiency and effectiveness in refugee status determination are compelling. Artificial intelligence allows for faster data processing and the ability to undertake high-volume, repetitive tasks. Increased consistency and up-to-date information, a capacity to plan for workloads and predict movements and the potential to ‘design out’ existing biases, promise to deliver positive outcomes for asylum seekers. But the risks of integrating artificial intelligence in a decision-making process that is defined by human vulnerability loom large. The lack of transparency in algorithms may result in a denial of procedural fairness, and algorithmic bias continues to be a vexing issue. If refugee and human rights are denied, international protection may be compromised. Technical and contextual issues may increase the potential for error, and unanswered questions remain around legality.
2. Alterman, A. 2003. "A piece of yourself: Ethical issues in biometric identification." Ethics and In-formation Technology, 5:139-150.
3. Administrative Appeals Tribunal (AAT 2019/20) Annual Report 2019/2020. https://www.transparency.gov.au/annual-reports/administrative-appeals-tribunal/reporting-year/2019-20-61 Accessed 10 March 2021.
4. Armed Conflict Location & Event Data Project (ACLED), 2020. https://www.acleddata.com/ Accessed 6 February 2020.
5. Barbour, B. 2018. "Refugee Status Determination Backlog Prevention and Reduction." UNHCR Divi-sion of International Protection, PPLA/ 2018/03 January 2018.
6. Bennett, J. 2017. "Myanmar: Rohingya refugees' future unclear as Bangladesh registers flood of arrivals." ABC, 26
https://www.abc.net.au/news/2017-09-26/rights-of-rohingya-in-question-bangladesh-myanmar/8987158 Accessed 11 April 2020.
7. Bennett Moses, LB. 2017. "Artificial Intelligence in the Courts, Legal Academia and Legal Prac-tice." Australian Law Journal, 91: 561-574.
8. Baker, S. Bowyer, K. Flynn, P. Phillips, J. 2013. "Template Aging in Iris Biometrics." in Burge M., Bowyer K. (eds) Handbook of Iris Recognition. Advances in Computer Vision and Pattern Recog-nition, Springer, London.
9. Bogen, M and Rieke, A. 2018. "Help Wanted: An Examination of Hiring Algorithms, Equity, and Bias." Upturn, December, 7 https://www.upturn.org/static/reports/2018/hiring-algorithms/files/Upturn%20--%20Help%20Wanted%20-%20An%20Exploration%20of%20Hiring%20Algorithms,%20Equity%20and%20Bias.pdf Ac-cessed 21 April 2020.
10. Bovens, M. 2007. “Analysing and Assessing Ac-countability: A Conceptual Framework.” Europe-an Law Journal 13: 447-468.
11. Bulman, A. 2019. "Asylum waiting times at rec-ord high as thousands ‘left in limbo" Independent 22 August
https://www.independent.co.uk/news/uk/home-news/asylum-seekers-waiting-times-home-office-immigration-a9075256.html Accessed 30/12/2019.
12. Brookland, J. 2019. "Revolutionising Recruit-ment: A test for AI in the United Nations https://medium.com/unhcr-innovation-service/revolutionising-recruitment-a-test-for-ai-in-the-united-nations-4456df0b1431 Accessed 10 February 2020.
13. Caribou Digital, 2018. "Identity at the Margins: Identification Systems for Refugees" https://assets.publishing.service.gov.uk/media/5cecedd6ed915d2475aca8c5/Identity-At-The-Margins-Identification-Systems-for-Refugees.pdf Accessed 1 April 2020.
14. Campion, M.A., Campion, M.C., Campion, E., Reider, M. 2016. "Initial Investigation Into Com-puter Scoring of Candidate Essays for Personnel Selection." The Journal of applied Psychology, 101(7): 958–975,
15. Chen, Z. and Whitney, D. 2019. "Tracking the af-fective state of unseen persons." Proceedings of the National Academy of Sciences 116 (15) 7559-7564, https://doi.org/10.1073/pnas.1812250116.
16. Cohen, J. 2001. "Questions of Credibility: Omis-sions, Discrepancies and Errors of Recall in the Testimony of Asylum Seekers." International Journal of Refugee Law, 13: 293-309.
17. Commonwealth Ombudsman, 2019. Automated Assistance in Administrative Decision-Making Better Practice Guide, https://www.ombudsman.gov.au/publications/better-practice-guides/automated-decision-guide Accessed 18 May 2020.
18. Country of Origin Information and Social Media: A Literature Review (Country Research Branch, Immigration New Zealand, October 2013) https://www.ecoi.net/site/assets/files/1890/crb-country-of-origin-information-and-social-media-executive-summary-october-2013.pdf Accessed 19 March 2021.
19. Costantini, S., DeGasperis, G., Olivieri, R. 2019. "Digital forensics and investigations meet artiﬁcial intelligence." Annals of Mathematics and Artiﬁcial Intelligence, 86: 193–229.
20. Corrigan, J. 2019. "DHS is Collecting Biometrics on Thousands of Refugees Who Will Never Enter the U.S." Nextgov, August https://www.nextgov.com/emerging-tech/2019/08/dhs-collecting-biometrics-thousands-refugees-who-will-never-enter-us/159310/ Accessed 3 January 2020.
21. Council of the European Union, 2010. The Stock-holm Programme – An Open and Secure Europe Serving and Protecting Citizens, OJ (2010/C 115/01).
22. Crisp, J. 2018. "Beware the notion that better da-ta lead to better outcomes for refugees and mi-grants." Chatham House, 9 March
https://www.chathamhouse.org/expert/comment/beware-notion-better-data-lead-better-outcomes-refugees-and-migrants Accessed 10 April 2020.
23. Curzon, R.E., Curotto P., Evason M., Failla A., Kusterer P., Ogawa A., Paraszczak J., Raghava S. “A unique approach to corporate disaster philan-thropy focused on delivering technology and ex-pertise.” IBM Journal of Research and Develop-ment, 64:1/2 January/March 2020.
24. Dalmasso, N., Mejia, R., Rodu, J., Price, M. and Murray J. 2019. "Feature Engineering for Entity Resolution with Arabic Names: Improving Esti-mates of Observed Casualties in the Syrian Civil War." Artiﬁcial Intelligence for Humanitarian As-sistance and Disaster Response Workshop, Neu-rIPS. https://www.cmu.edu/chrs/publications/pdf/ai_for_hadr_neurips_2019.pdf
Accessed 6 February 2020.
25. Dastin, J. 2018. "Amazon scraps secret AI recruit-ing tool that showed bias against women." Reu-ters, 10 October https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G Accessed 21 April 2020.
26. Dauvergne, C. and Millbank, J., 2003. "Burdened by Proof: How the Australian Refugee Review Tribunal has Failed Lesbian and Gay Asylum seekers." Federal Law Review, 31: 299-342.
27. Davenport, T.H. 2018. The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. Cambridge, US: MIT Press).
28. Department of Home Affairs, 2018. Technology Strategy 2020 (DHA 2018) https://www.homeaffairs.gov.au/reports-and-pubs/PDFs/%20technology-strategy-2020.pdf Accessed 6 March 2020.
29. Department of Immigration and Border Protec-tion, 2017. Delivering visa services for Australia; Market Consultation Paper, (DIBP 2017) https://immi.homeaffairs.gov.au/immigration-reform-subsite/files/delivering-visa-services-for-australia.pdf Accessed 2 January 2020.
30. Department of Immigration and Border Protec-tion, Request for Expression of Interest for Deliver-ing Visa Services for Australia – Bundle 1 DIBP REOI 22/17-B1, (DIBP 2017(2))
https://www.tenders.gov.au/atm/ShowClosed/5d617e2b-fb3a-a0e6-5ff5-1853c6971ef0?PreviewMode=False Accessed 2 January 2020.
31. Directive on Automated Decision-Making (2019) http://www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=32592 Accessed 21/12/2019.
32. Edmonson, C. 2019. "ICE Used Facial Recognition to Mine State Driver’s License Databases." The New York Times, 7 July https://www.nytimes.com/2019/07/07/us/politics/ice-drivers-licenses-facial-recognition.html Accessed 31 December 2019.
33. Ernst and Young, 2018. The new age: artificial in-telligence for human resource opportunities and functions https://hrlens.org/resources/2019/11/ey-the-new-age-artificial-intelligence-for-human-resource-opportunities-and-functions/ Accessed 18 May 2020.
34. Evans Cameron, H, 2018. Refugee Law's Fact Finding Crisis. Cambridge, UK: Cambridge Uni-versity Press.
35. Faliagka, E., Tsakalidis, A., Tzimas, G. 2012. "An integrated e‐recruitment system for automated personality mining and applicant ranking." Inter-net Research, 22(5): 551-568.
36. Feldman Barrett, L., Adolphs, R. Marsella, S., Martinez, A.M., Seth D. Pollak. 2019. "Emotional Expressions Reconsidered: Challenges to Infer-ring Emotion From Human Facial Movements." Psychological Science in the Public Interest 20 (1): 1-68,
37. Frischen, A., Bayliss, A., Tipper, S. 2007. "Gaze Cueing of Attention: Visual Attention, Social Cog-nition, and Individual Differences." Psychological Bulletin 133: 694-724,
38. Gallagher, R., Jona, L. 2019. "We Tested Europe’s New Lie Detector for Travelers — and Immedi-ately Triggered a False Positive." The Intercept, 26 July https://theintercept.com/2019/07/26/europe-border-control-ai-lie-detector/ Accessed 14 April 2020.
39. Grant, R.W. and Keohane, R.O. 2007. "Accounta-bility and Abuses of Power in World Politics." American Political Science Review, 99(1): 29-43.
40. Goldfarb, A. and Evans Cameron, H. “Artificial in-telligence for a reduction of false denials in refu-gee claims” (Working Paper, May 2020) https://d.io/sri-seminar-avi-goldfarb Accessed 29 December 2020.
41. Halliday, S. and Scott, C. 2010. "A Cultural Analy-sis of Administrative Justice." in Michael Adler (ed) Administrative Justice in Context. Hart Pub-lishing, UK.
42. Hathaway, J. and Foster, M. 2014. The Law of Refugee Status. Cambridge, UK: Cambridge Uni-versity Press, 2nd ed.
43. High-Level Expert Group on Artificial Intelli-gence: A definition of AI: main capabilities and scientific disciplines, 2018. (HLEGAI 2018)
http://www.pcci.gr/evepimages/0101_F483.pdf Accessed 8 October 2019.
44. Hvidtfeldt, C., Petersen, J.H., Norredam, M. 2019. "Prolonged periods of waiting for an asylum de-cision and the risk of psychiatric diagnoses: a 22-year longitudinal cohort study from Denmark." International Journal of Epidemiology, https://doi.org/10.1093/ije/dyz091.
45. Hartwig, M., Granhag, P. A., Strömwall, L. A., 2017. "Guilty and innocent suspects strategies during police interrogations." Psychology, Crime & Law, 13: 213-227.
46. Herlihy, J., Gleeson, K., Trurner, S. 2010. "What Assumptions about Human Behaviour Underlie Asylum Judgments?" International Journal of Refugee Law, 22(3): 351-366.
47. Herlihy, J., Scragg, P., Turner, S. 2002. "Discrep-ancies in autobiographical memories— implica-tions for the assessment of asylum seekers: re-peated interviews study," BMJ, 324(7333):324-327. https://doi.org/10.1136/bmj.324.7333.324
48. Galloway, A. "Government bins $1 billion visa outsourcing plan." Sydney Morning Herald, 20 March https://www.smh.com.au/politics/federal/government-bins-1-billion-visa-outsourcing-plan-20200320-p54cdb.html Accessed 30 March 2020.
49. Immigration and Refugee Board of Canada, 2004. Assessment of Credibility in Claims for Refugee Pro-
tection (IRBC 2004) https://irb-cisr.gc.ca/en/legal-policy/legal-concepts/Pages/Credib.aspx Accessed 15 No-vember 2020.
50. "International Conference on the Protection Mandate of UNHCR - Final Report" 1999. Peace Palace, the Hague, the Netherlands 18 September 1998. (ICPM 2018). 11 International Journal of Refugee Law 11: 397–416,
51. International Human Rights Program (Faculty of Law, University of Toronto) and the Citizen Lab (Munk School of Global Affairs and Public Policy, University of Toronto), “Bots at the Gate: A Hu-man Rights Analysis of Automated Decision-Making in Canada’s Immigration and Refugee System.” (Bots at the Gate 2018)
https://ihrp.law.utoronto.ca/sites/default/files/media/IHRP-Automated-Systems-Report-Web.pdf Accessed 12 November 2020.
52. Irons, A. and Lallie, H. 2014. "Digital Forensics to Intelligent Forensics." Future Internet 6(3), 584-596; https://doi.org/10.3390/fi6030584
53. Islam, M. Z. 2017. "Govt gets biometric software to list Rohingyas." The Daily Star, 11 September https://www.thedailystar.net/business/govt-gets-biometric-software-list-rohingyas-1460266 Accessed 10 April 2020.
54. Islam, M. Z. 2018. “Bangladesh faces refugee an-ger over term 'Rohingya', data collection.” Reu-ters, 26 November https://uk.reuters.com/article/uk-myanmar-rohingya-bangladesh/bangladesh-faces-refugee-anger-over-term-rohingya-data-collection-idUKKCN1NV1EI Accessed 11 April 2020.
55. Jarral, R. Kells, T. Lin, N. Y., Mason-Mackay, A., Sharma, K. 2019. "Artificial intelligence for hu-manitarian action: an interdisciplinary approach to communicable diseases in refugees" BMJ Leader 3: A19-A20.
56. Kagan, M. 2003. "Is Truth in the Eye of the Be-holder? Objective Credibility Assessment in Ref-ugee Status Determination." Georgetown Immi-gration Law Journal 17: 367–415.
57. Kairinos, N. 2019. "The integration of biometrics and AI," Biometric Technology Today:8-10 https://doi.org/10.35741/issn.0258-2718.104.22.168
58. Keshavarz, M., 2020. “Violent Compassions: Hu-manitarian Design and the Politics of Borders.” DesignIssues. 36(4): 21-32.
59. Kingston, L. N. 2018. "Biometric Identification, Displacement, and Protection Gaps." in Maitland, C.F. (ed) Digital Lifeline? ICTS for Refugees and Displaced Persons Cambridge. US: MIT Pres.
60. Kleinberg, B., van der Toolen, Y., Vrij, A., Arntz, A., Verschuere, B. 2018. "Automated verbal cred-ibility assessment of intentions: The model statement technique and predictive modeling." British Journal of Social Psychology, 32(3): 354-366. https://doi.org/10.1002/acp.3407
61. Larson, G. 2018. "How to use AI to fight identity fraud." Tech Beacon https://techbeacon.com/security/how-use-ai-fight-identity-fraud Accessed 13 November 2020.
62. Latonero, M. Kift, P. 2018. "On Digital Passages and Borders: Refugees and the New Infrastruc-ture for Movement and Control." Social Media + Society, January-March: 1–11
63. Lee, Y. 2019. "With Face Scans, Automated Mark-ing, Singapore Carves AI Niche" Bloomberg, 26 November
https://www.bloomberg.com/news/articles/2019-11-13/singapore-aims-to-carve-out-niche-in-ai-race-led-by-u-s-china Accessed 10 November 2020.
64. Martin, S. F. and Singh, L. 2018. "Data Analytics and Displacement: Using Big Data to Forecast Mass Movements of People." in Carleen F Mait-land (ed) Digital Lifeline? ICTS for Refugees and Displaced Persons. Cambridge, US: MIT Press: 185.
65. Marx, R. 1995. "Non-refoulement, Access to Pro-cedures and Responsibility for Determining Ref-ugee Claims." International Journal of Refugee Law 7: 383-406.
66. Mashaw, J. 2003. Bureaucratic Justice: Managing Social Security Disability Claims, Connecticut, Yale University Press.
67. Meaker, M. 2018. "Europe is using smartphone data as a weapon to deport refugees." Wired, 2 July https://www.wired.co.uk/article/europe-immigration-refugees-smartphone-metadata-deportations Accessed 13 January 2020.
68. Mitchell, F. 2010. "The use of Artificial Intelli-gence in Digital Forensics: An Introduction." Dig-ital Evidence and Electronic Signature Law Re-view 37: 35-41.
69. Myers West, S., Whittaker, M., Crawford, K. 2019. Discriminating Systems: Gender, Race, and Power in AI, AI Now Institute, New York University https://ainowinstitute.org/discriminatingsystems.pdf Accessed 24 April 2020.
70. National Center for Border Security and Immi-gration (NCBSC), 2013. "Field Tests of an AVA-TAR Interviewing System for Trusted Traveler Applicants"
Accessed 7 January 2020.
71. Norwegian ID Centre, 2016. "Misbruk av ID-dokumenter 2016." https://www.nidsenter.no/globalassets/dokumenter/publikasjoner/nid-rapporter/misbruktedok2016.pdf Accessed 14 January 2020.
72. Nunamaker, Jr. J.F., Burgoon, J.K., Twyman, N.W., Proudfoot, J.G., Schuetzler, R., Giboney, J.S. 2012. "Establishing a Foundation for Automated Hu-man Credibility Screening" Information Systems and Quantitative Analysis, (University of Nebras-ka, Faculty Proceedings & Presentations).
73. Office of Internal Oversight Services, Internal Audit Division, 2016 (OIOS 2016). Audit of the Biometric Identity Management System of the Of-fice of the High Commissioner for Refugees Report 2016/181.
74. Pauls, K. 2018. "It means everything to me': Wait times to have refugee claims heard continue to rise." CBC, 18 July
75. Papadopoulos, P. 2018. Digital Transformation and Visa Decisions: an insight into the promise and pitfalls https://www.nomos.com.au/wp-content/uploads/2018/10/AIAL-National-Conference-presentation-Technology-Data-and-Administrative-Law-28-Sep-2018.pdf Accessed 2 January 2020.
76. Pasquale, F. 2015. The Black Box Society: The Se-cret Algorithms That Control Money and Infor-mation Cambridge, US, Harvard University Press.
77. Perry, M. 2017. "iDecide: Administrative Deci-sion-Making in the Digital World." Australian Law Journal, 91: 29-34.
78. Poole, D.L. and Mackworth, A.L. 2017. Artificial Intelligence, Foundations of Computational Stud-ies. Cambridge, UK: Cambridge University Press, 2nd ed.
79. Rhue, L. 2018. "Racial Influence on Automated Perceptions of Emotions." SSRN:
https://ssrn.com/abstract=3281765 Accessed 13 November 2020.
80. Rogen, A., Rieke, A. 2018 "Help Wanted: An Ex-amination of Hiring Algorithms, Equity, and Bi-as." December, 35 https://apo.org.au/sites/default/files/resource-files/2018-12/apo-nid210071.pdf Accessed 15 January 2020.
81. Ruffer, G. B-A. 2018. "Information Components of Refugee Status Determination." in Maitland, C.F. (ed) Digital Lifeline? ICTS for Refugees and Dis-placed Persons. Cambridge, US: MIT Press 35.
82. Samuel, A. 2019. "Artificial intelligence will change e-discovery in the next three years." Law Technology Today, https://www.lawtechnologytoday.org/2019/04/artificial-intelligence-will-change-e-discovery-in-the-next-three-years
Accessed 12 January 2020.
83. Sidrane, C., Fitzpatrick, D.J., Annex, A., O'Dono-ghue, D., Gal, Y., & Bilinski, P. 2019. "Machine Learning for Generalizable Prediction of Flood Susceptibility." ArXiv, https://arxiv.org/abs/1910.06521 Accessed 5 February 2020.
84. Report of the Special Rapporteur on contempo-rary forms of racism, racial discrimination, xen-ophobia and related intolerance. 2020 (Special Rapporteur 2020). Note by the Secretary-General, UN Doc. No. A/75/590 (UN General As-sembly, Nov. 2020)
85. Sudman, S., Bradburn, N. 1973. "Effects of time and memory factors on response in surveys." Journal of the American Statistical Association 68: 805-815. https://doi.org/10.2307/2284504
86. Suleimenova, D. Bell, D. Groen, D. 2017. "A gen-eralized simulation development approach for predicting refugee destinations." Scientific Re-ports, 7: 13377, https://www.nature.com/articles/s41598-017-13828-9
87. Sweeney, J. 2009. "Credibility, Proof and Refugee Law." International Journal of Refugee Law, 21(4): 700-726, https://doi.org/10.1093/ijrl/eep027.
88. Tekle, T.A. 2020. "Refugees in Ethiopia's camps raise privacy and exclusion concerns over UN-HCR’s new digital registration." Global Voices, 19 March https://advox.globalvoices.org/ 2020/03/19/refugees-in-ethiopias-camps-raise-privacy-and-exclusion-concerns-over-unhcrs-new-digital-registration/ Accessed 9 April 2020.
89. Tucker, P. 2016 "Refugee or Terrorist? IBM Thinks Its Software Has the Answer." 27 January https://www.defenseone.com/technology/2016/01/refugee-or-terrorist-ibm-thinks-its-software-has-answer/125484/ Accessed 28/2/19.
90. Tomlinson, J., Sheridan, K., Harkens, A. 2019. "Proving Public Law Error in Automated Deci-sion-Making Systems." SSRN:
https://ssrn.com/abstract=3476657 Accessed 8 May 2020.
91. UK Border Agency, 2012. Assessing Credibility and Refugee Status, https://www.gov.uk/government/publications/considering-asylum-claims-and-assessing-credibility-instruction Accessed 16November 2020.
92. UK Home Office, 2015. Asylum Policy Instruction Assessing Credibility and Refugee status, v9.0 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/397778/ASSESSING_CREDIBILITY_AND_REFUGEE_STATUS_V9_0.pdf Accessed 15 November 2020.
93. UNHCR, 1998. Note on Burden and Standard of Proof in Refugee Claims, https://www.refworld.org/docid/3ae6b3338.html Accessed 14 November 2020.
94. UNHCR, 2001. Asylum Processes (Fair and Effi-cient Asylum Procedures), EC/GC/01/12, 31 May 2001.
95. UNHCR, 2008. ‘The Work of the Refugee Protec-tion Division in the International Context", Statement by Erika Feller, UNHCR, at the Immi-gration and Refugee Board, Refugee Protection Division National Training Seminar (Toronto, Canada, January 28-30, 2008), 28 January 2008.
96. UNHCR, 2011 (2). Quality in the Swedish Asylum Procedure: A Study of the Swedish Migration Board’s Examination of and Decisions on Applica-tions for International Protection.
97. UNHCR, 2015. Guidelines on International Pro-tection No. 11: Prima Facie Recognition of Refu-gee Status, HCR/GIP/15/11 https://www.refworld.org/pdfid/555c335a4.pdf Accessed 15 November 2020.
98. UNHCR, 2015 (2). Policy on the Protection of Per-sonal Data of Persons of Concern to UNHCR https://www.refworld.org/docid/55643c1d4.html Accessed 12 April 2020.UNHCR, 2019 (1). In-troductory Remarks of Andrew Harper Director of the Division of Programme Support & Manage-ment: Global Strategic Priorities, (EC/70/SC/CRP.13) 75th Meeting of the Stand-ing Committee (19 June 2019).
99. UNHCR, 2019 (2). "Data of millions of refugees now securely hosted in PRIMES" 28 January https://www.unhcr.org/blogs/data-millions-refugees-securely-hosted-primes/ Accessed 1 April 2020.
100. UNHCR, 2019 (3). Global Trends – Forced Dis-placement in 2019, https://www.unhcr.org/5ee200e37.pdf Ac-cessed 15 March 2021. UNHCR, 2019 (4). Hand-book on Procedures and Criteria for Determining Refugee Status under the 1951 Convention and the 1967 Protocol relating to the Status of Refu-gees UN Doc. HCR/IP/4/Eng/REV.3. https://www.unhcr.org/en-au/publications/legal/5ddfcdc47/handbook-procedures-criteria-determining-refugee-status-under-1951-convention.html Accessed 17 March 2021.
101. UNHCR, 2020. Procedural Standards for Refugee Status Determination under UNHCR’s Mandate. https://www.refworld.org/docid/5e870b254.html Accessed 11 March 2021.
102. UNHCR/EU, 2003. Beyond Proof: Credibility As-sessment in EU Asylum Systems, https://www.unhcr.org/51a8a08a9.pdf Ac-cessed 15 November 2020.
103. US Department of Justice, Immigration and Natu-ralization Service, 1998. Guidelines for Children's Asylum Claims (USJ 1998), https://www.refworld.org/docid/3f8ec0574.html Accessed 16 November 2020.
104. Wagstaff, E. and Tranter, K. 2014. “Taking Face-book at face value: The Refugee Review Tribu-nal’s
use of social media evidence.” Australian Journal of Administrative Law 21(3): 172-186.
105. Walsh, F.M. and Walsh, E.M. 2008. "Effective Processing or Assembly-Line Justice? The Use of Teleconferencing in Asylum Removal Hearings." Georgetown Immigration Law Journal, 22: 259-284.
106. Watson, D. 2019. "The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence." Minds & Machines, 29:417–440.
107. Weiss, E. 2020. "UNHCR Uses IrisGuard’s EyePay Tech to Distribute Funds to Refugees in Egypt." FindBiometrics, 24 February https://findbiometrics.com/biometrics-news-unhcr-uses-irisguards-eyepay-tech-distribute-funds-refugees-egypt-022407/ Accessed 1 April 2020.
108. Yearwood, J. 1997. “Case-based Retrieval of Ref-ugee Review Tribunal Text Cases.” In Legal Knowledge Based Systems. JURIX: The Tenth Con-ference, 67-83.
109. Yearwood, J. and Stranieri, A. 1999. “The integra-tion of retrieval, reasoning and drafting for refu-gee law: a third generation legal knowledge based system.” In Proceedings of the 7th interna-tional conference on Artificial intelligence and law, 117-125.
110. Zeleznikow, J. 2000. “Building Decision Support Systems in Discretionary Legal Domains.” Inter-national Review of Law, Computers & Technology, 14(3):341-356. https://doi.org/10.1080/713673368