TY  - JOUR
PY  - 2021//
TI  - The absorption and multiplication of uncertainty in machine-learning-driven finance
JO  - British journal of sociology
A1  - Hansen, Kristian Bondo
A1  - Borch, Christian
SP  - 1015
EP  - 1029
VL  - 72
IS  - 4
N2  - 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.<p />
LA  - en
SN  - 0007-1315
UR  - http://dx.doi.org/10.1111/1468-4446.12880
ID  - ref1
ER  -