Human/Machine(-Learning) Interactions - Human Agency and the International Humanitarian Law Proportionality Standard

Aug 3, 2026

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Developments in machine learning trigger questions about algorithmic decision-support systems in warfare. The article examines how such technologies affect legal reasoning practices in military targeting under International Humanitarian Law’s proportionality requirement, which balances expected incidental civilian harm against anticipated military advantage. By foregrounding human agency in this reasoning process, it evaluates whether interactions between commanders and algorithmic DSS alter agency and displace human judgment.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Introduction

Machine learning and military decision-support

International humanitarian law and proportionality

Human agency in legal reasoning

Commanders, supporting teams, and algorithmic tools

Reliability limits, biases, and opacity

UvA-DARE (Digital Academic Repository) Human/Machine(-Learning) Interactions, Human Agency and the International Humanitarian Law Proportionality Standard Woodcock, T. K. DOI 10.1080/13600826.2023.2267592 Publication date 2024 Document Version Final published version Published in Global Society License CC BY Link to publication Citation for published version (APA): Woodcock, T. K. (2024) . Human/Machine(-Learning) Interactions, Human Agency and the International Humanitarian Law Proportionality Standard. Global Society, 38(1), 100-121. [https://doi.org/10.1080/13600826.2023.2267592](https://doi.org/10.1080/13600826.2023.2267592) General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) . Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl)) Download date:27 May 2024 GLOBAL SOCIETY 2024, VOL. 38, NO. 1, 100–121 [https://doi.org/10.1080/13600826.2023.2267592](https://doi.org/10.1080/13600826.2023.2267592) Human/Machine(-Learning) Interactions, Human Agency and the International Humanitarian Law Proportionality Standard Taylor Kate Woodcock TMC Asser Instituut, University of Amsterdam, Amsterdam, Netherlands ABSTRACT Developments in machine learning prompt questions about algorithmic decision-support systems (DSS) in warfare. This article explores how the use of these technologies impact practices of legal reasoning in military targeting. International Humanitarian Law (IHL) requires assessment of the proportionality of attacks, namely whether the expected incidental harm to civilians and civilian objects is excessive compared to the anticipated military advantage. Situating human agency in this practice of legal reasoning, this article considers whether the interaction between commanders (and the teams that support them) and algorithmic DSS for proportionality assessments alter this practice and displace the exercise of human agency. As DSS that purport to provide recommendations on proportionality generate output in a manner substantively diﬀerent to proportionality assessments, these systems are not ﬁt for purpose. Moreover, legal reasoning may be shaped by DSS that provide intelligence information due to the limits of reliability, biases and opacity characteristic of machine learning. ARTICLE HISTORY Received 15 November 2022 Accepted 25 August 2023 KEYWORDS International humanitarian law; legal reasoning; human agency; machine learning; decision-support systems; proportionality 1. Introduction The past year has seen generative artiﬁcial intelligence (AI), such as Open AI’s ChatGPT, spark headlines and renew public interest in how AI can mediate our lives. In less than a year since the public release of ChatGPT on 30 November 2022, reports indicate the United States Department of Defense (US DoD) is experimenting with large language models from various Big Tech companies with a view to using “AI-enabled data indecision-making, sensors and ultimately ﬁrepower” in military operations (Manson 2023) . Indeed, the US DoD has already launched “Task Force Lima” to pu

FAQs

What proportionality standard does International Humanitarian Law require for attacks?

It requires assessing whether expected incidental harm to civilians and civilian objects is excessive compared to the anticipated military advantage.

How do algorithmic decision-support systems affect legal reasoning in targeting?

The article argues that when systems generate proportionality outputs in a substantially different manner, they are not fit for purpose and can reshape legal reasoning.

Why might machine-learning systems undermine human agency in proportionality assessments?

Because legal reasoning may be shaped by DSS that provide intelligence information affected by reliability limits, biases, and opacity characteristic of machine learning, potentially displacing human judgment.

Aug 3, 2026

Upload by Eliana