Machine Learning, Machine Sociality, and Sociological Theory

In this talk, I discuss what the advent of machine learning (ML) systems might mean for sociological theory. Drawing on fieldwork in financial markets, in which algorithmic trading founded on ML-based decision-making is gaining traction, I discuss the extent to which established sociological notions remain relevant or demand a reconsideration when applied to an ML context. I argue that ML systems are capable of making autonomous decisions and that this should lead to a broadening of sociological notions of agency so that the capacity for agency is also granted to ML systems. I further argue that collective machine behavior, in which ML systems interact with other machines, challenges established sociological notions of financial markets, including that of embeddedness.

Christian Borch is Professor of Economic Sociology and Social Theory at the Copenhagen Business School. He is the PI of the ERC-funded project “Algorithmic Finance.” His latest book is Social Avalanche: Crowds, Cities and Financial Markets (Cambridge UP, 2020).

Link to Zoom: https://www.score.su.se/forskning/evenemang/%C3%B6ppet-seminarium-vid-score-christian-borch-1.544174