Ashley Deeks

Machine Learning, Artificial Intelligence, and the Use of Force by States

CO-AUTHORS Noam Lubell and Daragh Murray
PUBLISHER
National Security Law & Policy
DATE
2019
 

Abstract

Big data technology and machine learning techniques play a growing role across all areas of modern society. Machine learning provides the ability to predict likely future outcomes, to calculate risks between competing choices, to make sense of vast amounts of data at speed, and to draw insights from data that would be otherwise invisible to human analysts. Despite the significant attention given to machine learning generally in academic writing and public discourse, however, there has been little analysis of how it may affect war-making decisions, and even less analysis from an international law perspective. The advantages that flow from machine learning algorithms mean that it is inevitable that governments will begin to employ them to help officials decide whether, when, and how to resort to force internationally. In some cases, these algorithms may lead to more accurate and defensible uses of force than we see today; in other cases, states may intentionally abuse these algorithms to engage in acts of aggression, or unintentionally misuse algorithms in ways that lead them to make inferior decisions relating to force.

This essay’s goal is to draw attention to current and near future developments that may have profound implications for international law, and to present a blueprint for the necessary analysis. More specifically, this article seeks to identify the most likely ways in which states will begin to employ machine learning algorithms to guide their decisions about when and how to use force, to identify legal challenges raised by use of force-related algorithms, and to recommend prophylactic measures for states as they begin to employ these tools.

Citation

Ashley S. Deeks, Noam Lubell & Daragh Murray, Machine Learning, Artificial Intelligence, and the Use of Force by States, 10 National Security Law & Policy 1-25 (2019).
 

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