Machine learning is becoming increasingly central to academic research, yet it often depends on data workers in exploitative conditions whose contributions are largely overlooked in ethical guidelines and unacknowledged within the academic community.
Last year, the Methods Lab outlined the aims of a project to target this issue in a short blog post. We’re now excited to announce the resulting published discussion paper: “Ethics of Data Work: Principles for Academic Data Work Requesters.”
This paper builds on the insights of an interdisciplinary group of scholars, practitioners, and data workers, alongside expert workshops held at the Weizenbaum Institute in 2024. It organizes practical principles for engaging more ethically with platform-based data workers, including how to define data work to then address important gaps in current ethical guidelines. The paper therefore offers concrete recommendations and regulations based on the most pressing challenges faced by these contributors. As the rapid development of AI continues to rely on the insight and labor of real people, it’s crucial to reflect on how research is conducted to ensure those workers receive proper acknowledgment for their role. This discussion paper calls for commitment to fair treatment, transparency, and meaningful support to make ethical data work a consistent part of the machine learning research process.
If you would like to learn more about the experiences and working conditions of these data workers, check out our blog post featuring creative projects from the Data Workers’ Inquiry!