AI systems rely heavily on workers who face precarious conditions. Data work, clickwork, and crowdwork—essential for validating algorithms and creating datasets to train and refine AI systems—are frequently outsourced by commercial entities and academic institutions. Despite the vast and growing workforce of 435 million data workers enabling machine learning, their working conditions remain largely unaddressed, resulting in exploitative practices. Academic clients, in particular, lack clear guidance on how to outsource data work ethically and responsibly.
To address this issue, Christian Strippel from the Methods Lab is part of the short project “Ethics of Data Work” together with Milagros Miceli and Tianling Yang from the research group “Data, Algorithmic Systems and Ethics“, Bianca Herlo and Corinna Canali from the research “Design, Diversity and New Commons“, and Alexandra Keiner from the research group “Norm Setting and Decision Processes“. Together they aim to create equitable working systems grounded in the real knowledge and experience of data workers. The project will gather valuable insights about the challenges and needs data workers face, with the objective of developing ethical guidelines for researchers to ensure responsible and ethical treatment in the future.