Anne Washington, New York University; Deborah A. Anderson, University of Alberta; Kevin Lee, University of British Columbia; Michael Lounsbury, University of Alberta;
How does artificial intelligence disrupt the social order established through institutions? Institutions are sites of both trust and contested legitimacy but always omnipresent as carriers of the public interest. Artificial intelligence, big data, machine learning, and new forms of data science directly disrupt our assumptions about institutional structures along with the speed, scope, and scale of control. Expertise is both everywhere and more concentrated in artifacts of knowledge production that maintain existing social orders. Driven by open data sources, access to AI tools is now widespread across many layers of social power and sometimes outside the locus of traditional policy mechanisms. Scholars have highlighted that AI crosses boundaries between public and private in a network that unites platforms, governments, corporations, and finance in tight configurations. The entanglement of organizing, data, and quantification asks new questions of our existing theories and critical approaches to governance including legitimacy, risk, institutional logics, datafication, trust, and scientific development. This swiftly evolving new empirical material has made conceptual and theoretical clarity elusive, particularly across academic domains. Building on the conference theme of solidarity, we seek to cross disciplinary boundaries to better understand data-driven technoscientific configurations. Inspired by work from both STS and institutional scholars, this session welcomes contributions that tackle the nexus of these two literatures. We invite reflections on what institutional theory can learn from STS in this AI moment and what STS can learn from institutional theory.
Keywords: Disciplines and the Social Organization of Science and Technology, Forms and Practices of Expertise, Big Data, AI, and Machine Learning, institutional theory risk platform governance open data quantification data policy