Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data “prospecting” and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science. Prospecting aims to render the data, knowledge, expertise, and practices of worldly domains available and tractable to data science method and epistemology. Prospecting precedes data synthesis, analysis, (...) or visualization, and is constituted by the upstream work of discovering disordered or inaccessible data resources, thereafter to be ordered and rendered available for computation. Through this work, data science positions itself in the middle of all things—capable of engaging this, that, or any domain—and thus prospecting is a key driver of data science’s ongoing formation as a universal science. (shrink)

Given the complexity of teams involved in creating AI-based systems, how can we understand who should be held accountable when they fail? This paper reports findings about accountable AI from 26 interviews conducted with stakeholders in AI drawn from the fields of AI research, law, and policy. Participants described the challenges presented by the distributed nature of how AI systems are designed, developed, deployed, and regulated. This distribution of agency, alongside existing mechanisms of accountability, responsibility, and liability, creates barriers for (...) effective accountable design. As agency is distributed across the socio-technical landscape of an AI system, users without deep knowledge of the operation of these systems become disempowered, unable to challenge or contest when it impacts their lives. In this context, accountability becomes a matter of building systems that can be challenged, interrogated, and, most importantly, adjusted in use to accommodate counter-intuitive results and unpredictable impacts. Thus, accountable system design can work to reconfigure socio-technical landscapes to protect the users of AI and to prevent unjust apportionment of risk. (shrink)

Knowledge produced by environmental scientists is often inaccessible, intractable, or otherwise in need of reconfiguration for use in environmental regulation. Similarly, policy knowledge undergoes decontextualization in its address to the community of researchers and data curators whose findings are fundamental to its operation. This paper addresses the development of the total maximum daily load measurement as a means of decontextualizing both scientific and regulatory processes to render the practical results of those processes available as a means of collaboration, coordination, and (...) development of watershed management and regulation. The TMDL measurement serves as a specific type of boundary object, a provisional boundary figure. A provisional boundary figure is a complex, model-derived system that is not fixed but rather an object of ongoing work that enables coordination between policy and research. Thus, the TMDL is deployed to explore the relationship between environmental management and the knowledge workers that enable and support it. (shrink)