Stop Saying "AI"
Nathan G. Wood (1,2,3), Scott Robbins (4), Eduardo Zegarra Berodt (1), Anton Graf von Westerholt (1), Michelle Behrndt (1,5), Hauke Budig (1), Daniel Kloock-Schreiber (1) ((1) Institute of Air Transportation Systems, Hamburg University of Technology

TL;DR
This paper argues that the broad term "AI" hampers meaningful debate, especially in military contexts, and advocates for more precise terminology to clarify the specific systems, benefits, and risks involved.
Contribution
It provides a taxonomy of military AI systems and emphasizes the importance of specificity in AI debates to improve clarity and policy discussions.
Findings
Critiques of one AI system type do not necessarily apply to others.
Precise terminology enhances understanding and policy formulation.
Broad "AI" debates often obscure specific system challenges and benefits.
Abstract
Across academia, industry, and government, ``AI'' has become central in research and development, regulatory debates, and promises of ever faster and more capable decision-making and action. In numerous domains, especially safety-critical ones, there are significant concerns over how ``AI'' may affect decision-making, responsibility, or the likelihood of mistakes (to name only a few categories of critique). However, for most critiques, the target is generally ``AI'', a broad term admitting many (types of) systems used for a variety of tasks and each coming with its own set of limitations, challenges, and potential use cases. In this article, we focus on the military domain as a case study and present both a loose enumerative taxonomy of systems captured under the umbrella term ``military AI'', as well as discussion of the challenges of each. In doing so, we highlight that critiques of…
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Taxonomy
TopicsEthics and Social Impacts of AI · Adversarial Robustness in Machine Learning · Human-Automation Interaction and Safety
