Social data science is still a young domain of knowledge. This course will introduce to some of the fundamental questions that have been raised in this domain across the social sciences. Some of these questions are relatively new, such as access to commercial data where the nature of how the data are processed is not known. Others are perennial questions that have been reignited with computational approaches, such as the scientific nature or otherwise of the social sciences. These questions will be examined in relation to concrete cases or examples of research. For each case or example, we will examine three major themes: (i) the epistemological or scientific validity of big data approaches, (ii) their ethical and social aspects or implications, and (iii) their practical implications and limits.

Learning Objectives

Upon completion of this class, students will:

  • Understand the major debates arising from social data science
  • Assemble convincing evidence to support intellectual argument
  • Have a thorough understanding of the social, legal, and ethical issues of data science ap-proaches to knowledge generation
  • Be familiar with findings and claims made using social data science approaches
  • Understand emerging areas where data science approaches are likely to play a major role in society

Topics

  1. Big Data and (Free Information) Markets
  2. Big Data and (Social) Science
  3. Algorithmic Fairness and Bias
  4. Algorithmic Transparency and Explainability
  5. Privacy and Regulation in Data Science
  6. Governance and Professionalism in Social Data Science
  7. Big Data and Development
  8. Big Data and Measuring the Public’s Responses to Climate Change
This page was last modified on 29 October 2019