Ethics of Data Work. Principles for Academic Data Work Requesters

Abstract

The growing use of machine learning (ML) in academic research has led to a rising demand for large, labeled datasets. While the field initially relied on the labor of students and research assistants to label data, as models grew larger and more complex, there was a shift towards relying on large-scale, low-cost platforms like Amazon Mechanical Turk (MTurk) to label data at scale. However, this shift comes with serious ethical concerns. Now part of a massive industry, many data work companies exploit workers, leaving many workers facing low wages and precarious working conditions, with little institutional oversight or protection. Despite the centrality of this labor to modern research, ethical codes and guidelines from academic societies rarely address the implications of outsourcing data work to platform-based workers. This paper advocates for the development of research ethics standards that ensure fair and responsible collaboration with data workers. We begin by defining the concept of “data work” and assessing how current ethical frameworks address it. We then highlight ongoing initiatives aimed at improving ethical regulation. Based on two focus groups and two expert workshops held at the Weizenbaum Institute in 2024, we propose a set of principles for academic data work requesters to guide ethical engagement with platform-based workers. Finally, we outline future steps for integrating these principles into scientific ethical codes and day-to-day research practices.