Department of Artificial Intelligence and Machine Learning
| Course Name | Artificial Intelligence and Ethics | ||
| Semester | Course Code | Theory Practice Hours | ECTS |
| 4 | YPZ303 | 3 / 0 | 4 |
| Course Level | Bachelor's Degree | ||
| Course Delivery Format | Face to face | ||
| Coordinator | Assoc. Prof. Dr. Hakan Yılmaz | ||
| Coordinator E-mail | hyilmaz ktun.edu.tr | ||
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| Course Objective | The aim of this course is to enable students to evaluate the artificial intelligence systems they develop or use not only in terms of technical performance, but also with respect to their ethical, legal, societal, and professional implications; to identify risks that may arise throughout the AI lifecycle; and to make reasoned decisions in accordance with responsible AI principles. |
| Eng Temel Meslek Dersi | Eng Uzmanlaşma Dersi |
| 60 | 40 |
| Instructional Methods |
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| Lecture, Discussion, Question and Answer, Case Study, Problem Solving, Brain Storming |
| Week | Course Content |
|---|---|
| 1 | Introduction to AI Ethics and Ethical Decision-Making |
| 2 | Scientific and Professional Ethics and the Responsible AI Lifecycle |
| 3 | Data Ethics, Privacy, Consent, and Data Governance |
| 4 | Algorithmic Bias, Fairness, and Inclusiveness |
| 5 | Transparency, Explainability, Accountability, and AI Evaluation |
| 6 | Artificial Intelligence, Human Rights, Surveillance, and Democratic Society |
| 7 | AI Governance, Regulations, and Standards |
| 8 | Midterm Exam |
| 9 | AI and Society: Work, Education, Healthcare, and Human–AI Relationships |
| 10 | Generative AI, Creativity, Copyright, and Synthetic Media |
| 11 | AI Safety and Cybersecurity |
| 12 | Agentic AI, Autonomy, and High-Risk Decision-Making |
| 13 | The Future of AI: Alignment, AGI, Power, and Sustainability |
| 14 | Case Workshop |
| 15 | Final Exam |
| No | Course Outcomes (CO) | Percent |
|---|---|---|
| 1 | Identify ethical, legal, societal, and professional issues arising in artificial intelligence systems, as well as the relevant stakeholders. | 15 |
| 2 | Analyze artificial intelligence systems in terms of privacy, bias, fairness, transparency, explainability, and accountability. | 25 |
| 3 | Evaluate the potential impacts of artificial intelligence applications on individuals, society, human rights, and the environment, and make reasoned judgments regarding associated ethical risks. | 20 |
| 4 | Apply artificial intelligence governance, regulatory, and responsible AI approaches in the evaluation of real-world applications. | 15 |
| 5 | Analyze real-world artificial intelligence cases by jointly considering ethical, technical, societal, and legal dimensions, and propose solutions within the framework of professional responsibility. | 25 |
| CO 1 | CO 2 | CO 3 | CO 4 | CO 5 | |
|---|---|---|---|---|---|
| PO 1 | 0 | 2 | 0 | 0 | 0 | PO 2 | 3 | 5 | 3 | 2 | 7 | PO 3 | 0 | 0 | 0 | 3 | 4 | PO 4 | 0 | 0 | 0 | 0 | 0 | PO 5 | 0 | 0 | 0 | 0 | 0 | PO 6 | 0 | 0 | 0 | 0 | 0 | PO 7 | 0 | 0 | 0 | 0 | 0 | PO 8 | 0 | 0 | 0 | 3 | 0 | PO 9 | 6 | 6 | 5 | 7 | 7 | PO 10 | 0 | 0 | 3 | 0 | 0 | PO 11 | 6 | 7 | 9 | 5 | 7 |
| ECTS Workload | Number | Duration,Min | Total Workload |
|---|---|---|---|
| Weekly Lecture Hour (Theoretical) | 14 | 135 | 1890 | Weekly Practice/Lab Hour | 0 | 0 | 0 | Homework | 0 | 0 | 0 | Quiz | 0 | 0 | 0 | Material Design, Preparation | 0 | 0 | 0 | Field Studies | 0 | 0 | 0 | Report Preparation | 0 | 0 | 0 | Presentation | 0 | 0 | 0 | Project Making | 0 | 0 | 0 | Other Studies | 0 | 0 | 0 | Mid-Term exam and pre-study | 1 | 1500 | 1500 | Final Exam and pre-study | 1 | 3500 | 3500 |
| ECTS Workload | % of Mid-term Grading | % of Final Grading |
|---|---|---|
| Weekly Lecture Hour (Theoretical) | 0 | 0 | Weekly Practice/Lab Hour | 0 | 0 | Homework | 0 | 0 | Quiz | 0 | 0 | Material Design, Preparation | 0 | 0 | Field Studies | 0 | 0 | Report Preparation | 0 | 0 | Presentation | 0 | 0 | Project Making | 0 | 0 | Other Studies | 0 | 0 | Mid-Term exam and pre-study | 100 | 0 | Final Exam and pre-study | 0 | 100 |