TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF SCIENCE AND LETTERS |
| ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING PR. (ENGLISH) | |
| Code | YZZ112 |
| Name | Ethics Artificial Intelligence |
| Term | 2025-2026 Academic Year |
| Semester | 2. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Belirsiz |
| Type | Normal |
| Label | C Compulsory |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi Cevher ÖZDEN |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to identify, analyze, and propose solutions to ethical issues encountered in the development and implementation of artificial intelligence technologies. The aim is to instill in students a sense of professional responsibility within the framework of fundamental ethical theories and the ability to assess societal impacts. We will discuss how issues such as data privacy, bias, transparency, accountability, and human rights should be addressed in AI systems. Furthermore, by examining national and international ethical standards, students will be encouraged to be mindful of ethical design and decision-making processes.
Course Content
What is ethics? Philosophical foundations and fundamental ethical theories (deontology, utilitarianism, virtue ethics). Professional ethics, engineering ethics, and ethics in the context of artificial intelligence engineering. Professional responsibilities and ethical codes of conduct of artificial intelligence developers. The impact of professional standards of conduct on artificial intelligence systems. Codes of Ethics for Artificial Intelligence (NSPE, ACM, IEEE, EU AI Act, etc.) and international ethical principles. Ethical principles and legal regulations within the framework of the European Union Artificial Intelligence Regulation (AI Act). The impacts of artificial intelligence on society: employment, justice, bias. Ethical responsibilities in the context of social media, surveillance, and public safety. Artificial intelligence in combating social justice, inclusivity, and social inequalities. Conflicts of interest, transparency, explainability, and accountability. Ethical and legal responsibilities: Potential harms, unforeseen consequences. Privacy, data ethics, big data, and user consent. Cybersecurity threats, fraud, misleading content, and algorithmic bias. Ethical decision-making models, multi-stakeholder assessments, and case studies.
Course Precondition
None
Resources
Stahl, B. C. (2021). Artificial intelligence for a better future: an ecosystem perspective on the ethics of AI and emerging digital technologies (p. 124). Springer Nature.
Notes
1. Vieweg, S. H. (2021). AI for the Good. Springer International Publishing. 2. Fleddermann, Charles B. 2012; Engineering Ethics, Fourth Edition. Pearson. 3. Charles E. Harris, Michael S. Pritchard, and Michael J. Rabins. 2019; Engineering Ethics: Concepts and Cases, Cengage Learning. CENGAGE.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Can explain basic ethical concepts and theories. |
| LO02 | Be able to identify and analyze ethical issues encountered in artificial intelligence applications. |
| LO03 | Can interpret national and international artificial intelligence ethical principles and professional ethical codes. |
| LO04 | Can apply ethical decision-making models to artificial intelligence projects. |
| LO05 | Can make ethical evaluations by taking into account possible social, legal and cultural impacts on society. |
| LO06 | Can develop solutions to ethical problems through real-world case studies. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | It provides a broad range of knowledge about fundamental Computer Science concepts, algorithms and data structures. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Learns basic computer topics such as software development, programming languages, and database management. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Understands advanced computing fields such as data science, artificial intelligence, and machine learning. | |
| PLO04 | Belirsiz | Learn about topics such as computer networks, cyber security, and database design. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Develops skills in designing, implementing and analyzing algorithms. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Gains the ability to use different programming languages effectively | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Learns data analysis, database management and big data processing skills. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Gains practical experience by working on software development projects. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Strengthens collaboration and communication skills within the team. | 3 |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | It provides a mindset open to technological innovations. | 3 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Encourages continuous learning and self-improvement competence. | 4 |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Develops the ability to solve complex problems. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | What is ethics? Philosophical foundations and basic ethical theories | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım, Beyin Fırtınası |
| 2 | Professional ethics and ethics in the field of artificial intelligence | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Professional responsibilities and ethical codes of conduct of AI developers | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım |
| 4 | The impact of professional conduct standards on artificial intelligence systems | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 5 | Artificial Intelligence Professional Ethics Codes (NSPE, ACM, IEEE, EU AI Act etc.) and international ethical principles | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım |
| 6 | Ethical principles and legal regulations within the framework of the European Union Artificial Intelligence Regulation (AI Act) | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | The impact of artificial intelligence on society: employment, justice, bias | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 8 | Mid-Term Exam | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Ethical responsibilities in the context of social media, surveillance, and public safety | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım |
| 10 | Artificial intelligence in social justice, inclusivity and combating social inequalities | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 11 | Conflicts of interest, transparency, explainability and accountability | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 12 | Ethical and legal responsibilities: Possible harm situations, unforeseen consequences | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım |
| 13 | Privacy, data ethics, big data and user consent | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 14 | Cybersecurity threats, fraud, misleading content, and algorithmic bias | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 15 | Ethical decision-making models, multi-stakeholder assessments and case studies | Reading relevant lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 16 | Final Exams | Final Exams | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Final Exams | Final Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 26 | 26 |
| Total Workload (Hour) | 122 | ||
| Total Workload / 25 (h) | 4,88 | ||
| ECTS | 5 ECTS | ||