ABSTRACT
This chapter tackles the theme of algorithmic accountability and regulation in social data research. It acknowledges a tension between traditional ethics oversight (like institutional review board protocols) and the realities of big-data studies, where data are often repurposed from contexts outside of research. The chapter argues that ensuring ethics in big data projects requires context-sensitive approaches – a “one-size-fits-all” mindset is inadequate. For example, using personal health app data for research raises different concerns than analyzing public economic data, so ethical decisions must account for the data’s origin and intended use. A key point in the chapter is that researchers must maintain accountability for AI and algorithm-driven findings. Even if an AI system autonomously analyzes data, human researchers are responsible for its outcomes. To prepare future researchers, this chapter introduces the concept of “ethical literacy” – the idea that doctoral training should include building competence in ethics and data governance, not just technical skills. Ultimately, the chapter advocates for stronger interdisciplinary cooperation (among technologists, ethicists, and policymakers) to develop adaptive governance mechanisms for data-intensive research.
