| School/Faculty/Institute | Faculty of Law | |||||||
| Course Code | LAW 229 | |||||||
| Course Title in English | AI Law | |||||||
| Course Title in Turkish | Yapay Zekâ Hukuku | |||||||
| Language of Instruction | EN | |||||||
| Type of Course | Lecture | |||||||
| Level of Course | Intermediate | |||||||
| Semester | ||||||||
| Contact Hours per Week |
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| Estimated Student Workload | 98 hours per semester | |||||||
| Number of Credits | 4 ECTS | |||||||
| Grading Mode | Standard Letter Grade | |||||||
| Pre-requisites | None | |||||||
| Expected Prior Knowledge | None | |||||||
| Co-requisites | None | |||||||
| Registration Restrictions | Undergraduate Students Only | |||||||
| Overall Educational Objective | Students will learn the basic concepts on AI law. | |||||||
| Course Description | The course aims to give the students a basic understanding of opportunities challenges presented by the newly emerging AI technology while exploring the intersection of artificial intelligence (AI) and the legal system. Students will examine the ways in which AI technology is impacting various areas of law, including predictive policing, criminal justice, intellectual property and privacy. The course will focus on contemporary attempts for codification, particularly the EU AI Act and the Council of Europe Framework Convention on AI. Other areas of interest shall be the utilization of AI for predictive policing, intelligence gathering, commercial purposes, market and election manipulation. Possibilities on future uses of AI shall be explored, such as the use of AI in legal professions, coding existing law into algorithms, and the use of brain-machine interfaces in criminal justice. | |||||||
| Course Description in Turkish | Bu ders, öğrencilere yapay zeka (YZ) ve hukuk sisteminin kesişiminde yeni ortaya çıkan YZ teknolojisinin sunduğu fırsatlar ve zorluklar hakkında temel bir anlayış kazandırmayı amaçlamaktadır. Öğrenciler, yapay zeka teknolojisinin öngörücü polislik, ceza adaleti, fikri mülkiyet ve özel hayat dahil olmak üzere hukukun çeşitli alanlarını nasıl etkilediğini inceleyeceklerdir. Ders, özellikle AB YZ Yasası ve Avrupa Konseyi YZ Çerçeve Sözleşmesi olmak üzere güncel kodifikasyon çabalarına odaklanacaktır. Diğer ilgi alanları, YZ'nin tahmine dayalı polislik, istihbarat toplama, ticari amaçlar, piyasa ve seçim manipülasyonu için kullanılması olacaktır. Yapay zekânın hukuk mesleklerinde kullanımı, mevcut hukukun algoritmalaştırılması ve ceza adaletinde beyin-makine arayüzlerinin kullanılması gibi YZ'nin gelecekteki kullanımlarına ilişkin olasılıklar araştırılacaktır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Understand the fundamental principles of artificial intelligence technology and its applications within the legal field. 2) Analyze and evaluate the ethical and legal implications of AI technology within different areas of law, including criminal justice, intellectual property, privacy, and corporate regulations. 3) Develop the skills to effectively communicate and advocate for solutions to problems caused by the use of AI technology regarding human rights. 4) Demonstrate key elements and features of recent codification attempts regarding AI. 5) Comment on the use of predictive algorithms and theit implications on personal privacy. 6) Discuss issues regarding the widespread use of large language models and their implications on intellectual property. 7) Collaborate with peers to discuss and debate the complex issues surrounding AI technology for manipulative market and voter behavior and its legal implications. 8) Reflect on the evolving nature of AI technology and its impact on the legal profession, and consider the implications for future legal practice and policy development. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|
| 1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | ||||||||
| 2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | ||||||||
| 3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | ||||||||
| 4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | ||||||||
| 5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | ||||||||
| 6) Internalization and dissemination of professional ethical standards. | ||||||||
| 7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | ||||||||
| 8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | ||||||||
| 9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | ||||||||
| 10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | ||||||||
| 11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | ||||||||
| 12) Ability to acquire knowledge independently, and to plan one’s own learning. | ||||||||
| 13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
| 2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
| 3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
| 4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
| 5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
| 6) | Internalization and dissemination of professional ethical standards. | N | |
| 7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
| 8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
| 9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
| 10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
| 11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
| 12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
| 13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
| Prepared by and Date | , |
| Course Coordinator | RAĞIP BARIŞ ERMAN |
| Semester | |
| Name of Instructor | Asst. Prof. Dr. RAĞIP BARIŞ ERMAN |
| Week | Subject |
| 1) | Introduction to AI Technology and Legal Implications |
| 2) | Codification of AI: Challenges and Opportunities |
| 3) | EU AI Act |
| 4) | Council of Europe Framework Convention on AI |
| 5) | Privacy Concerns and AI Technology |
| 6) | AI in Intellectual Property Law |
| 7) | Predictive Policing and Intelligence Gathering |
| 8) | AI in Criminal Justice System |
| 9) | AI and Deepfakes |
| 10) | AI and Election Manipulation |
| 11) | Future of AI in Legal Professions |
| 12) | Algorithms and the Law |
| 13) | Brain-Machine Interfaces |
| 14) | Wrap-up and Discussion on Future Trends |
| Required/Recommended Readings | EU AI Act Council of Europe Framework Convention on artificial intelligence and human rights, democracy, and the rule of law | |||||||||||||||
| Teaching Methods | Lecture, student presentations & open class discussion | |||||||||||||||
| Homework and Projects | ||||||||||||||||
| Laboratory Work | ||||||||||||||||
| Computer Use | ||||||||||||||||
| Other Activities | ||||||||||||||||
| Assessment Methods |
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| Course Administration |
ermanr@mef.edu.tr |
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| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 1 | 2 | 2 | 70 | ||
| Presentations / Seminar | 2 | 6 | 2 | 2 | 20 | ||
| Final Examination | 1 | 6 | 2 | 8 | |||
| Total Workload | 98 | ||||||
| Total Workload/25 | 3.9 | ||||||
| ECTS | 4 | ||||||