Stuart Geiger (UC Berkeley)

The Human Contexts of Computation and Data: Infrastructures, Institutions, and Interpretations

The statistical techniques and computational infrastructures of artificial intelligence and data science are increasingly built into products, platforms, organizations, and institutions of all kinds. Yet the collection, curation, and analysis of data has always been as social as it is technical. Even in the most automated, “data-driven” systems, there is always human labor in designing, developing, deploying, documenting, debating, maintaining, managing, manipulating, training, triaging, translating, using, and not using such systems. In focusing on the human contexts of computation and data across the pipeline, we gain key insights into various issues across fields, as well as new possibilities for collaboratively producing knowledge. I will discuss several cases from my ethnographic research empirically studying institutions and infrastructures that support the production and distribution of knowledge. These include: how Wikipedians automate quality control while seeking to keep humans in the loop and uphold their principles of openness and decentralization; how targets of coordinated harassment campaigns on Twitter developed tools to help moderate their own experiences; the academic career paths of those who practice and support data science; the sustainability of open source communities that develop and maintain key software tools; and the interpretation of findings made from large-scale analyses of social data.

About the Speaker

Stuart Geiger is a staff ethnographer and postdoctoral scholar at the UC-Berkeley Institute for Data Science. He studies the infrastructures and institutions that support the production of knowledge, focusing on how advances in computation and data are changing how we know what we know. He completed his Ph.D at UC-Berkeley in the School of Information and the Berkeley Center for New Media, and his work has been published in venues including CSCW, CHI, ICWSM, American Behavioral Scientist, Information, Communication & Society, and Big Data & Society. He is a methodological and disciplinary pluralist, integrating approaches from across the humanities, the interpretivist and quantitative social sciences, and computer, information, and data science. Stuart is also a founding member of UC-Berkeley’s cross-departmental working groups on Data Science Studies, Algorithms in Culture, and Algorithmic Fairness & Opacity.