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.
Originalsprog | Engelsk |
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Tidsskrift | British Journal of Sociology |
Vol/bind | 72 |
Udgave nummer | 4 |
Sider (fra-til) | 1015-1029 |
ISSN | 0007-1315 |
DOI | |
Status | Udgivet - 2021 |
Udgivet eksternt | Ja |
Emneord
- Det Samfundsvidenskabelige Fakultet