International Relations (English) Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code:

INR039

Course Name:

Artificial Intelligence and Migration Governance

Semester:

Fall
Spring

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Departmental Elective

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator:

Prof. Dr. AYSELİN GÖZDE YILDIZ OĞUZALP

Course Lecturer(s):

Prof. Dr. Ayselin YILDIZ

Course Assistants:

Course Objective and Content

Course Objectives:

The aim of this course is to examine and debate how artificial intelligence is and can be utilized in migration governance. As an interdisciplinary course, it aims to provide students comparative knowledge by examining the implications of AI in both migration policy/practice (border management, entry/exit practices, visa, voice/face/fingerprint recognition, natural language understanding (NLU) etc.) and also geographical diversification (country cases).

Course Content:

This course covers the use of artificial intelligence (AI) in the context of migration governance with a focus on current debates and practices as well as its ethical aspects.

Learning Outcomes

The students who have succeeded in this course;
1) Identify AI based key systems and contemporary approaches in migration governance.
2) Critically analyse the use of AI in border management, visa policy and international protection.
3) Develop suggestions for ethical use of AI in migration related efficient decision making.

Course Flow Plan

Week	Subject	Related Preparation
1)	Understanding AI as a Key Technology in Digital Transformation	Guest Lecture from ISU AI Lab.
2)	Overview: Migration and AI	Ana Beduschi, ‘International Migration Management in the Age of Artificial Intelligence’ (2020). IOM World Migration Report (2022) Chapter 4 Chapter 11 - Artificial intelligence, migration and mobility: implications for policy and practice
3)	Use of AI in border controls (unmanned drones, land and maritime surveillance), securitization of migration?	European Commission, ‘Opportunities and challenges for the use of artificial intelligence in border control, migration and security’ (2020).
4)	Use of AI in International Protection	Ludivine Sarah Stewart, Fair and efficient asylum procedures and artificial intelligence: Quo Vadis due process?, Computer Law & Security Review, Volume 55, 2024. Meltem Ineli-Ciger, Resettlement by algorithm: Can artificial intelligence uphold human rights?, Computer Law & Security Review, Volume 55, 2024.
5)	Use of AI in visa processing: Biometrics, entry and exit practices. Case: Artificial Intelligence (AI) at Schengen Borders: Automated Processing, Algorithmic Profiling and Facial Recognition Case: Practices at Istanbul Airport (e-gates)	Jennifer Lynch, ‘Face off report – Law enforcement use of facial recognition technology’ (Electronic Frontier Foundation, 2020) 7. Javier Galbally Herrerro et al, ‘Study on Face Identification Technology for its Implementation in the Schengen Information System’ (2019) 9. Vavoula, N. (2021). Artificial Intelligence (AI) at Schengen Borders: Automated Processing, Algorithmic Profiling and Facial Recognition in the Era of Techno-Solutionism, European Journal of Migration and Law, 23(4), 457-484.
6)	AI and human trafficking/migrant smuggling Guest Lecture Panel (Lecturers will be invited from ISU Dep.of Management Information Systems, Visual Communication Design, and Migration Management Presidency)
7)	Fundamental Rights Approach and Ethical Aspects	Petra Molnar, ‘Technology on the Margins: AI and Global Migration Management from a Human Rights Perspective’ (2019) 8(2) Cambridge International Law Journal 305
8)	Opinion Paper Submission (Mid-term) Determination of Teams and Case Selection Violations of the right to privacy, data protection and procedural rights	Raji, Deborah ‘How our data encodes systematic racism’, MIT Technology Review, 10 December 2020, https://www.technologyreview.com/2020/12/10/1013617/racism-data-science-artificial-intelligence-ai-opinion/ Lauren Rhue, ‘Emotion-reading tech fails the racial bias test’, The Conversation, 3 January 2019, https://theconversation.com/emotion-reading-tech-fails-the-racial-bias-test-108404 UN Special Rapporteur on Contemporary forms of racism, racial discrimination, xenophobia and related intolerance, Report to the 75th session of the UN General Assembly. November 2020, https://undocs.org/A/75/590
9)	Use of AI in Student Mobility and Higher Education: Early Recognition Systems for Prior Learning and Refugees without documents (Guest Lecture)	Yildiz, Ayselin (2019). “Integration of Refugee Students in European Higher Education: Comparative Country Cases”. ISBN: 987-975-6339-71-8.
10)	Alternatives to Administrative Detention and Use of AI (voice recognition systems, remote biometric identification, natural language understanding)
11)	AI-based individual risk assessment and profiling systems, ETIAS Regulation	Statewatch, ‘Automated suspicion: The EU’s new travel surveillance initiatives’ (2020). Susie Alegre, Julien Jeandesboz and Niovi Vavoula, ‘European Travel Information and Authorisation System (ETIAS): Border Management, Fundamental Rights and Data Protection’ (Study for the LIBE Committee of the European Parliament, 2017) 23–26.
12)	Case Examination: 1. iBorderCtrl project 2. Automated Virtual Agent for Truth Assessments in Real-Time (AVATAR) Frontex risk analysis:
13)	How to Utilize AI in More Efficient and Evidence-based Decision Making in Migration? (Visa applications, asylum applications, administrative detention, public security, prediction of new migration)
14)	Feedback process for the final project submission, active participation in class, presentations
15)	Submission of the final project as project teams

Sources

Course Notes / Textbooks:

Petra Molnar. (2024). The Walls Have Eyes: Surviving Migration in the Age of Artificial Intelligence. ISBN: 978-1-62097-836-8. Lecture notes, presentations

Ana Beduschi, ‘International Migration Management in the Age of Artificial Intelligence’ (2020).

IOM World Migration Report (2022) Chapter 4 Chapter 11 - Artificial intelligence, migration and mobility: implications for policy and practice

European Commission, ‘Opportunities and challenges for the use of artificial intelligence in border control, migration and security’ (2020).

Ludivine Sarah Stewart, Fair and efficient asylum procedures and artificial intelligence: Quo Vadis due process?,
Computer Law & Security Review, Volume 55, 2024.

Meltem Ineli-Ciger, Resettlement by algorithm: Can artificial intelligence uphold human rights?, Computer Law & Security Review, Volume 55, 2024.

Jennifer Lynch, ‘Face off report – Law enforcement use of facial recognition technology’ (Electronic Frontier Foundation, 2020) 7.

Javier Galbally Herrerro et al, ‘Study on Face Identification Technology for its Implementation in the Schengen Information System’ (2019) 9.

Vavoula, N. (2021). Artificial Intelligence (AI) at Schengen Borders: Automated Processing, Algorithmic Profiling and Facial Recognition in the Era of Techno-Solutionism, European Journal of Migration and Law, 23(4), 457-484.

Raji, Deborah ‘How our data encodes systematic racism’, MIT Technology Review, 10 December 2020, https://www.technologyreview.com/2020/12/10/1013617/racism-data-science-artificial-intelligence-ai-opinion/

Lauren Rhue, ‘Emotion-reading tech fails the racial bias test’, The Conversation, 3 January 2019, https://theconversation.com/emotion-reading-tech-fails-the-racial-bias-test-108404

UN Special Rapporteur on Contemporary forms of racism, racial discrimination, xenophobia and related intolerance, Report to the 75th session of the UN General Assembly. November 2020, https://undocs.org/A/75/590

Yildiz, Ayselin (2019). “Integration of Refugee Students in European Higher Education: Comparative Country Cases”. ISBN: 987-975-6339-71-8.

Statewatch, ‘Automated suspicion: The EU’s new travel surveillance initiatives’ (2020).

Susie Alegre, Julien Jeandesboz and Niovi Vavoula, ‘European Travel Information and Authorisation System (ETIAS): Border Management, Fundamental Rights and Data Protection’ (Study for the LIBE Committee of the European Parliament, 2017) 23–26.

Matthias Leese, ‘The New Profiling: Algorithms, Black Boxes, and the Failure of Anti-discriminatory Safeguards in the European Union’ (2014) 45(5) Security Dialogue 494

Petra Molnar and Lex Gill (2018). “Bots at the Gate: A Human Rights Analysis of Automated Decision-Making in Canada’s Immigration and Refugee System”.

References:

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3
Program Outcomes
1) Has knowledge about basic theoretical debates in the field of International Relations.
2) Defines contemporary developments, approaches and basic concepts in the field of International Relations at national and international level.
3) Relates the interaction of the Department of International Relations with other social sciences (history, political science, law, economy).
4) Gains knowledge and skills to evaluate and discuss the events in an interdisciplinary dimension, and analyze the political, social and economic problems encountered in national and international contexts within a certain conceptual framework.
5) Using social sciences methods, can conduct research and follow the field.
6) Uses leadership characteristics with awareness of teamwork.
7) Can design and prepare scientific projects such as projects, reports, articles and theses with their own or others with uses qualitative and quantitative research techniques.
8) Develops behavior according to ethics and social values and evaluates what they have learned by deciding what he/she needs and critically question the information has acquired.
9) Transmits the opinions, thoughts and solutions in the field of International Relations to the related persons and institutions in written and orally.
10) Follows the developments in the field and communicates with colleagues by using a foreign language at least at the level of European Language Portfolio B1.
11) Uses information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Has knowledge about basic theoretical debates in the field of International Relations.
2)	Defines contemporary developments, approaches and basic concepts in the field of International Relations at national and international level.
3)	Relates the interaction of the Department of International Relations with other social sciences (history, political science, law, economy).
4)	Gains knowledge and skills to evaluate and discuss the events in an interdisciplinary dimension, and analyze the political, social and economic problems encountered in national and international contexts within a certain conceptual framework.
5)	Using social sciences methods, can conduct research and follow the field.
6)	Uses leadership characteristics with awareness of teamwork.
7)	Can design and prepare scientific projects such as projects, reports, articles and theses with their own or others with uses qualitative and quantitative research techniques.
8)	Develops behavior according to ethics and social values and evaluates what they have learned by deciding what he/she needs and critically question the information has acquired.
9)	Transmits the opinions, thoughts and solutions in the field of International Relations to the related persons and institutions in written and orally.
10)	Follows the developments in the field and communicates with colleagues by using a foreign language at least at the level of European Language Portfolio B1.
11)	Uses information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Attendance	14	% 0
Presentation	1	% 20
Midterms	1	% 20
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Preparation for the Activity	Spent for the Activity Itself	Completing the Activity Requirements	Workload
Course Hours	14	3			42
Presentations / Seminar	4	5			20
Midterms	7	5			35
Final	12	5			60
Total Workload					157