HSUHK | CCCO

SDS3000 Global Data Citizenship

Department
School of Decision Sciences
Semester
AY2023/24 Sem 1, AY2023/24 Sem 2, AY2024/25 Sem 1
Method
Lecture 3 hours
Cluster
2 (Social Sciences)

MOOC

Learning Mode 1.5 hours Online learning + 1.5 hours Face-to-face teaching

Prerequisite

GEN1000 Perspectives on General Education

Exclusion

Nil

Module Description

Recent figures show that human and machine generated data is experiencing an overall 10x faster growth rate than traditional business data, and machine data is increasing even more rapidly at 50x the growth rate. The acquisition and analysis of data significantly affect almost every aspect of our daily decisions, from personal finance to political election. Because of the vital importance of data, data quality, technology ethics and decision making in this data world have become alarming global concerns. Through this module, students will not only identify and examine the data related global issues but also enhance their decision making and critical thinking abilities as responsible global citizens.

Module Intended Learning Outcomes (MILO)

Upon completion of this module, students should be able to:
a. Discuss data citizenship in a globalized world.
b. Identify and assess core issues of data citizenship in a global context.
c. Examine the application and implication of emerging data technologies addressing contemporary global issues.
d. Develop attitude of care and responsibility of data citizenship.

Module Content

1. Introduction

1.1 Framework of Data Citizenship
1.2 Concept of Data Responsibility

2. Nature and Applications of Data

2.1 Data Source and Quality
2.2 Data Veracity
2.3 Fake Data
2.4 Impact of Data and its Technology to the World

3. Technology Ethics in Data World

3.1 Current Technologies
3.2 Ethics in Technology
3.3 Data Security and Data Privacy
3.4 Social Responsibility

4. Decision Making in Data World

4.1 Impact of Technology on Global Issues
4.2 Logical Thinking in Data World
4.3 Scientific Management
4.4 Group Decision by Voting

Assessment Methods

1. Participation (10%)
2. Assignment(s) (40%)
3. Group Project (50%)

Texts & References

  1. I. Foster, R. Ghani, R. S. Jarmin, F. Kreuter, J. Lane, Big Data and Social Science: A Practical Guide to Methods and Tools, Chapman and Hall/CRC, 2020
  2. J. Lane, V . Stodden, S. Bender, H. Nissenbaum, Privacy, Big Data, and the Public Good: Frameworks for Engagement, Cambridge University Press, 2014.
  3. M. J. Salganik, Bit by Bit: Social Research in the Digital Age, Princeton University Press, 2017.
  4. K. Shu, S. Wang, D. Lee, H. Liu, Disinformation, Misinformation, and Fake News in Social Media, Springer, 2020.
  5. G. W. Reynolds, Ethics in information technology (6th ed.), Boston, Mass.: Cengage Learning, 2018.
  6. K. Mitnick, The Art of Invisibility: The World’s Most Famous Hacker Teaches You How to Be Safe in the Age of Big Brother and Big Data, Back Bay Books, 2019.
  7. J. Journel, Data & Analytics 4.0: The Future of Work, Privacy and Trust in the Age of Artificial Intelligence, Insert Analytics, 2019.
  8. S.N. Chiu, L. Ling, Mathematics of Fairness, The Hong Kong Mathematical Society, 2010
  9. A. R. Angel, C. D. Abbott, D. C. Runde, A Survey of Mathematics with Application (11th Ed), Boston: Pearson, 2021.
  10. J. R. Evans, Total Quality – Management, Organization and Strategy (4th Ed), Thomson, 1993.
  11. D. M. Levine, P. P. Ramsey, R. K. Smidt, Applied Statistics for Engineers and Scientists, Prentice Hall, 2013.
  12. United Nations, Department of Economic and Social Affairs, Sustainable Development, https://sdgs.un.org/topics.