ABSTRACT
No other topic has stirred up as much controversy and division as the ethics of computational social science research. For one, massive amounts of data are readily available for download in the form of digital trace data on individuals and their interactions, providing opportunities for unprecedented insight into social science research questions. Complicating matters is that developments in computational approaches and methods are rapid, while policy, legal frameworks, ethics guidelines, and pedagogy lag behind. In this chapter, we discuss four major challenges that computational social science scholars confront with regard to ethical questions and dilemmas, often linked to data interpretation and user privacy: 1) data representativity, 2) aggregation and disaggregation, 3) research data archiving, and 4) data linkage. In addition, we also provide a roadmap for computational social science scholars to address these issues using best practices informed by exemplary research. We conclude by stressing the relevance of building on existing ethics guidelines and integrating new legal frameworks into research practice. We argue that central to computational social science is a constant engagement with ethics and new regulations as well as an active commitment to pedagogy, to teaching the principles of ethics in computational social science projects.
