Citation

Hansen KB, Borch C. Br. J. Sociol. 2021; 72(4): 1015-1029.

Copyright

(Copyright © 2021, London School of Economics and Political Science, Publisher John Wiley and Sons)

DOI

10.1111/1468-4446.12880

PMID

unavailable

Abstract

Uncertainty about market developments and their implications characterize financial markets. Increasingly, machine learning is deployed as a tool to absorb this uncertainty and transform it into manageable risk. This article analyses machine-learning-based uncertainty absorption in financial markets by drawing on 182 interviews in the finance industry, including 45 interviews with informants who were actively applying machine-learning techniques to investment management, trading, or risk management problems. We argue that while machine-learning models are deployed to absorb financial uncertainty, they also introduce a new and more profound type of uncertainty, which we call critical model uncertainty. Critical model uncertainty refers to the inability to explain how and why the machine-learning models (particularly neural networks) arrive at their predictions and decisions?their uncertainty-absorbing accomplishments. We suggest that the dialectical relation between machine-learning models? uncertainty absorption and multiplication calls for further research in the field of finance and beyond.

Keywords

algorithms; economic sociology; financial models; machine learning; uncertainty