Interrogating Data Science

Abstract

Data science provides powerful tools and methods. CSCW researchers have contributed insightfulstudies of conventional work-practices in data science - and particularly machine learning. However,recent research has shown that human skills and collaborative decision-making, play important rolesin defining data, acquiring data, curating data, designing data, and creating data. This workshopgathers researchers and practitioners together to take a collective and critical look at data sciencework-practices, and at how those work-practices make crucial and often invisible impacts on theformal work of data science. When we understand the human and social contributions to data sciencepipelines, we can constructively redesign both work and technologies for new insights, theories, andchallenges.