DATA PRIVACY AND ETHICS

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

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 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
2. P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
3. P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
4. P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
5. P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
6. P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
7. P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
8. P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
9. P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
10. P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.

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