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About the Programme Academic Staff Program Qualifications Lessons Matrix of Course & Program Qualifications Turkish Qualifications Framework (TQF) TQF & Program Qualifications
  1. Vocational School of Information Technologies
  2. Department of Software, Application Development and Analysis
  3. Program in Front-End Software Development
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  5. Introduction to AI Ethics
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Last Updated: 01/07/2026

Code - Course Title Course Type Laboratory + Practice ECTS
ÖYG122 - Introduction to AI Ethics II. SEMESTER 2 +0 3.0
Language of Instruction Türkçe
Course Type Area Elective Courses
Course Instructor(s) ÖĞR. GÖR. AHMET AVCIOĞLU
Mode of Delivery Face to face.
Place of Delivery
Used Educational Platforms
Prerequisites None.
Courses Recomended
Recommended Resources Lecture slides and practice notes.
Work Placement
Aims of the Course

Weeks Learning Outcomes Topics Teaching Methods
Week - 1 Course introduction, presentation of course structure and assessment method, the place of artificial intelligence in social life, the importance of ethical debates and an overview of the course scope.
Week - 2 Introduction to ethical theories, utilitarianism, deontology and virtue ethics, the application of these theories in the context of technology and artificial intelligence.
Week - 3 Fundamental concepts of artificial intelligence ethics, responsible artificial intelligence principles, trustworthy artificial intelligence frameworks, the human centered design approach.
Week - 4 Algorithmic bias and discrimination, data and model sources of bias, real world discrimination examples, evaluation of social consequences.
Week - 5 Fairness metrics and bias mitigation approaches, data diversity, model auditing, introduction to fair machine learning approaches.
Week - 6 Transparency and explainability, the black box problem, interpretable models, explainable artificial intelligence tools and the auditability of decision processes.
Week - 7 Privacy and data protection, the concept of personal data, consent and disclosure, data governance, regulations on the protection of personal data.
Week - 8 Midterm examination.
Week - 9 Automation and employment, the effects of artificial intelligence on the workforce, the transformation of professions, the skills gap and lifelong learning.
Week - 10 Social inequality and the digital divide, justice in access to artificial intelligence, the effect of technology on disadvantaged groups, inclusive design.
Week - 11 Generative artificial intelligence and copyright, content ownership, intellectual property rights, disinformation, deepfake content and issues of social trust.
Week - 12 Autonomous systems and responsibility, accountability in self deciding systems, the sharing of responsibility, safety and oversight.
Week - 13 Artificial intelligence regulations and policies, the European Union AI act, national and international policies, the risk based regulation approach.
Week - 14 Artificial intelligence governance and social responsibility, corporate ethics policies, ethical impact assessment, case study presentations and general recap.

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Assessment Method and Passing Requirements
Quamtity Percentage (%)
Toplam (%)

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