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SUMMARY:What Machines Reveal about the Principle of Proportionality - Naom
 i Korem\, Tammar Shrot\, Hadassa Daltrophe
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DTSTAMP:20260726T090825Z
UID:pretalx-iacap-2026-SQL7NB@pretalx.iacapconf.org
DESCRIPTION:The Principle of Proportionality is a cornerstone of Just War 
 Theory\, yet the advent of Lethal Autonomous Weapon Systems has intensifie
 d debates regarding its application. Conventional scholarship suggests pro
 portionality requires qualitative\, subjective judgment\, which algorithmi
 c systems inherently lack. Our talk reframes the inquiry\, proposing that 
 the difficulty of encoding proportionality into machines is not a technica
 l failure but an epistemological "mirror" reflecting the principle's funda
 mental conceptual instability. We proceed in two stages. First\, we provid
 e theoretical and empirical reasons to suspect the comprehensibility of th
 e proportionality principle. Second\, we analyze various machine learning 
 paradigms\, including supervised\, unsupervised\, and reinforcement learni
 ng\, to show that the inability to formalize a stable objective function p
 revents machines from learning the principle (a.k.a the "alignment problem
 ").\nWe argue that if a principle cannot be taught or formalized\, its com
 prehensibility to humans must be rigorously reconsidered. Rather than reve
 aling machine limitations\, the problem of teaching  Lethal Autonomous Wea
 pon Systems proportionality highlights the inherent indeterminacy of propo
 rtionality itself.
LOCATION:Executive Conference Room
URL:https://pretalx.iacapconf.org/iacap-2026/talk/SQL7NB/
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