@article{ref1,
title="The absorption and multiplication of uncertainty in machine-learning-driven finance",
journal="British journal of sociology",
year="2021",
author="Hansen, Kristian Bondo and Borch, Christian",
volume="72",
number="4",
pages="1015-1029",
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.<p />",
language="en",
issn="0007-1315",
doi="10.1111/1468-4446.12880",
url="http://dx.doi.org/10.1111/1468-4446.12880"
}