# Stop Saying "AI"

arXiv: https://arxiv.org/abs/2602.17729 · HTML: https://mlsanity.com/paper/2602.17729/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 221 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025)
- ['It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions](https://mlsanity.com/paper/1801.10408/) (2018)
- [Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples](https://mlsanity.com/paper/2510.07192/) (2025)
- [EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM System](https://mlsanity.com/paper/2509.10540/) (2025)
- [Edge AI: A Taxonomy, Systematic Review and Future Directions](https://mlsanity.com/paper/2407.04053/) (2024)
- [(Im)possibility of Automated Hallucination Detection in Large Language Models](https://mlsanity.com/paper/2504.17004/) (2025)
- [The wall confronting large language models](https://mlsanity.com/paper/2507.19703/) (2025)
- [Strategic Intelligence in Large Language Models: Evidence from evolutionary Game Theory](https://mlsanity.com/paper/2507.02618/) (2025)
- [Explainable and Human-Grounded AI for Decision Support Systems: The Theory of Epistemic Quasi-Partnerships](https://mlsanity.com/paper/2409.14839/) (2024)

## Built on by (most cited first, 1 of 1)

- [Practical Judgment, Virtue, and Intuition in the Use of Opaque AI-Enabled Systems](https://mlsanity.com/paper/2607.12755/) (2026)

## Abstract (arXiv)

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 one (type of) system will not always transfer to other (types of) systems. Building on this, we argue that in order for debates to move forward fruitfully, it is imperative that the discussions be made more precise and that ``AI'' be excised from debates to the extent possible. Researchers, developers, and policy-makers should make clear exactly what systems they have in mind and what possible benefits and risks attend the deployment of those particular systems. While we focus on AI in the military as an exemplar for the overall trends in discussions of ``AI'', the argument's conclusions are broad and have import for discussions of AI across a host of domains.