At the Design Lab, 4pm. Details here.
Abstract: 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.