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Cyprus International University
TR

DATA PRIVACY AND ETHICS

Course
DASC482 - DATA PRIVACY AND ETHICS
Department
Data Science - English - Undergraduate
Course Type
Course
Status
Required
Language
English
Credit
3
ECTS
7
T+P+L
3 + 0 + 0
Course Coordinator(s)
Asst. Prof. Dr. Sara SALEHI
Prerequisite
-
Keywords

Course Description

This course explores the many ethical dilemmas that arise in the contemporary practice of data science and creates a framework for comprehending these concerns. The impact of unethical behavior and how data are used ethically in society will be covered. The Findable, Accessible, Interoperable, and Reusable (FAIR) data principles are described. By the end of the course, students will be able to recognize and carefully differentiate between problems unique to data science and those that arise as a result of using other related computational methods of analysis, such as machine learning and artificial intelligence. They will also be able to distinguish between ethical misconduct unique to data science methods and those that may result from the contexts in which these methods are used.

DATA PRIVACY AND ETHICS

Evaluation Tools (Active Term)

No evaluation items have been defined.

Course outcomes

No course outcomes have been defined yet.

Course Syllabus

Week Topic
Week 1 Motivation for Ethics in Computing 1) Foundations of Ethics in Technology. 2) Morality vs. the Law
Week 2 Motivation for Ethics in Computing 3). Ethical Frameworks in Computing (utilitarianism, deontology, virtue ethics, etc.) and their application
Week 3 Ethics and the Professions 1) Intellectual property rights (IPR) 2) Software Risks and Liability
Week 4 Ethics and the Professions 3) Professional Codes 4) Generative AI
Week 5 Applicability of Ethics in Various Domains 1) Ethics in Artificial Intelligence (AI) 2) Ethics in Virtual Reality (VR) and Augmented Reality (AR)
Week 6 Applicability of Ethics in Various Domains 3) Ethics in Big Data and Predictive Analytics 4)Ethics in Social Media and Online Platforms
Week 7 Review- Seminar1
Week 8 Midterm Examination
Week 9 Motivation for Data Privacy 1) Importance of Data Privacy 2) Cultural and Legal Perspectives on Privacy 3) Examples of Privacy Violations 4) Generative AI as a Case Study
Week 10 Engineering Privacy 1) Privacy by Design (PbD) 2) Privacy and Data Protection Principles
Week 11 Contemporary Privacy Enhancing Techniques 1) Privacy Features in Authentication Protocols 2) Secure Private Communications 3) Communication Anonymity and Pseudonymity 4) Storage Privacy 5) Privacy-preserving tools for users
Week 12 Legal Frameworks for Privacy 1) Global Privacy Laws and Regulations 2) Principles including informed consent, data portability, and the right to be forgotten. 3) Compliance and Ethics: Meeting legal requirements while balancing business interests.
Week 13 Data Ethics 1) Principles of data ethics: (privacy, consent, fairness, transparency, accountability, data security) 2) Mitigating Bias: Reducing unfair outcomes through ethical, inclusive, and unbiased data practices.
Week 14 Review- Seminar2
Week 15 Final Exam

Reference Books & Course Materials

  1. 01 Loukides, M., Mason, H., & Patil, D. J. (2018). Ethics and data science. " O'Reilly Media, Inc.".
  2. 02 Payton, T., & Claypoole, T. (2023). Privacy in the age of Big data: Recognizing threats, defending your rights, and protecting your family. Rowman & Littlefield Publishers.
  3. 03 Slussareff, M. (2022). O'Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
  4. 04 Kearns, M., & Roth, A. (2019). The ethical algorithm: The science of socially aware algorithm design. Oxford University Press.

Learning Outcomes

  1. L01 Explain fundamental ethical concepts, principles, and frameworks relevant to computing, data science, and emerging technologies. SOLO 2
  2. L02 Apply ethical frameworks and professional codes to ethical dilemmas in computing and data-related practices. SOLO 3
  3. L03 Analyze ethical issues related to artificial intelligence, big data, predictive analytics, social media, virtual/augmented reality, and generative AI. SOLO 3
  4. L04 Evaluate the ethical implications of data collection, processing, sharing, and use with respect to privacy, fairness, transparency, consent, and accountability. SOLO 4
  5. L05 Assess the ethical and legal responsibilities associated with intellectual property, software risks, professional practice, and data protection. SOLO 4
  6. L06 Design ethically responsible approaches to data and technology use that incorporate privacy protection, bias mitigation, security, and principles such as FAIR data. SOLO 4

Program Outcomes

  1. P01 Create a user interface in a contemporary object-oriented language to allow users to access business data
  2. P02 Identify and analyze user needs and take them into account in the selection, creation, integration, evaluation and administration of computing-based systems
  3. P03 Analyze common business functions and identify, design, and develop appropriate information technology solutions
  4. P04 Design and develop software solutions for contemporary business environments by employing appropriate problem-solving strategies
  5. P05 Configure and administer database server to support contemporary business environments.
  6. P06 Administer or mange a relational database for a small to medium size company
  7. P07 Be able to effectively integrate IT-based solutions into the user environment
  8. P08 Understand professional, ethical, legal, security and social issues and responsibilities
  9. P09 Be able to apply effective communication skills consistent with the professional environment
  10. P10 Be able to apply effective collaboration skills in teamwork consistent with the professional environment

Po-Lo Matrix

LO P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 Average
L01 - - - - - - - - - - -
L02 - - - - - - - - - - -
L03 - - - - - - - - - - -
L04 - - - - - - - - - - -
L05 - - - - - - - - - - -
L06 - - - - - - - - - - -