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Session Submission Type: Traditional (Closed) Panel
Algorithms – loosely defined as a set of rules to direct the behavior of machines or humans – have shaped infrastructures, practices, and daily lives around the world. This panel explores the implications of algorithms from an STS perspective, guided by these questions:
(1) The multiple definitions and histories of algorithms: The term ‘algorithm’ predates the digital computer by over a millennium, with an etymology traceable to Islamic scholar al-Khwārizmī. How broadly might we usefully define the term today? Are contemporary algorithms necessarily computational? What are the implications of the explosion of discourse about ‘algorithms’ in popular culture?
(2) Algorithms as more than computation: What does it mean to study algorithms as myth, narrative, ideology, discourse, or power? How can these approaches contribute back to computer and data science?
(3) Algorithms as specifically computational: What are the social and theoretical implications of new developments in computation such as big data, deep neural networks, distributed computing, or ‘microwork’?
(4) Practices and materialities of algorithms: Just as science studies advocates for a focus on the local practices and material artifacts which produce and sustain scientific knowledge, what kinds of work is done to make algorithms computable, and what are the material effects of algorithms?
(5) Living with algorithms, quantifying the self: Algorithms pervade daily life and we experience their impact almost anywhere, not just at a computer. How can we better understand how far-flung domains are being reshaped by algorithms, and what are the implications in everyday and civic life?
Seeing Like an Algorithm: Machine Learning and the New Division of Apperceptive Labor - Thomas Gilbert
Understanding Perception of Algorithmic Decisions: The Case of Algorithmic Management - Min Kyung Lee, Carnegie Mellon University
Constitutions of the Human in the Digital Age, Then and Now - Margarita Boenig-Liptsin, Harvard STS
Fetishization Revisited: Faith and Notions of Efficacy in the Making of Algorithms - Dawn Nafus, Intel; Suzanne L. Thomas, Intel Labs; Jamie Sherman, Intel Labs