Algorithms at Work: The Practice of Prediction - I: Algorithms-in-Practice in Institutional and Organizational Perspective

Abstract

This session seeks to bring into conversation scholars across several disciplines who are interested in examining the relationship between work practices and machine algorithms. Concerned with “algorithms in practice" (Christin's (2017), the session will begin by exploring how our increasing reliance on these automation and prediction platforms is shaping, and shaped by, larger institutions, such as the legal system, education, and healthcare. We will then look at comparative ethnographic studies that examine the relationship between organizational structure/context and patterns in platform governance and user engagement. The overarching theme of the session is: How is decision-making affected by algorithmic data processing and machine learning, and with what organizational and institutional implications?

Chair

• Ifeoma Ajunwa, Cornell IRL School

Discussant

• Ifeoma Ajunwa, Cornell IRL School

Individual Presentations

• Constraining Learning, Expanding Competencies: Lawyer-in-the-Loop in Algorithmic Discovery - Fernando Delgado, Cornell University

• Evidence in AI: Predictive Algorithms in Healthcare - Anne Henriksen, Aarhus Univeristy; anja bechmann, UCI & Aarhus University

• Beyond Optimism: The Cruel ‘Iron Cage’ of Data Science in Education - Caroline Mason, Rensselaer Polytechnic Institute - STS

• Making Platforms Work: Conceptualizing Platform Labor and the Management of Publics - Benjamin Shestakofsky, University of Pennsylvania; Shreeharsh Kelkar, University of California, Berkeley

Session Organizer

• Sarah E. Sachs, Cornell University