Standardizing Language and Data Is Crucial for Responsible AI in Defense

Today’s headlines focus on killer robots and doomers, but the most pressing risk in military AI is more mundane: the people shaping it lack a common lexicon and shared data.

The discussion is not missing; the authors have taken part in many global deliberations, both as conveners and as private‑sector representatives. They can report that forging responsible governance of AI in the military domain will require shared definitions and a way to fuse relevant developments and decisions into something approximating “ground truth.”

Stakeholders span governments, militaries, the private sector, academia, the legal system, civil society, human‑rights groups, technology firms, journalists, and more. In multilateral negotiations and everyday conversation, they speak varied languages—both literally and topically. Their incentives differ: some are eager to pursue AI‑military integration, while others seek to regulate it. A word like “autonomy” means something different to a general, an engineer, a lawyer, and a human‑rights advocate, not to mention the nuances that arise from the roughly 200 nations they represent.

The staggering breadth of these players, combined with the wide range of AI applications, means that data about their actions is scattered. It can be found in government contract announcements, legislation, news coverage, court documents, patent applications, and beyond, in nearly every global language. This makes it impossible to track and compare developments within and across countries.

Without common language and data, humanitarian organizations often talk past supply‑chain experts, tech executives fail to communicate effectively with academic researchers, and efforts to develop responsible AI‑military integration cannot move forward.

This concern is not new. In 2024, the Responsible AI in the Military Domain Summit (REAIM) urged stakeholders to begin “fostering a common understanding of…capabilities and limitations [of AI‑military integration], and a shared understanding of the possible applications…and their potential impacts, including both benefits and risks.” The same year, the United Nations Institute for Disarmament Research Roundtable for AI, Security, and Ethics identified six priority areas for governance, including “Building a shared and solid knowledge base on AI in the military domain, developed through cross‑regional, inter‑disciplinary and multi‑stakeholder input.” And a 2023 report by the Global Commission REAIM urged states to “maintain international repositories of best practices, including AI concepts and definitions, AI capabilities in specific contexts of application…and military AI policies” to support regulatory efforts.

One effort to bridge the data gap is the AI‑Military Integration Database (AIMID), operated by a private company whose CEO is an author. The database performs a daily scan for news and information about AI‑military integration in more than 20 languages—news reports, company announcements, academic articles, legislation, contracts, think‑tank reports, and more. The result is an ever‑growing knowledge graph: a structured, footnoted map of actors, technologies, events, and the relationships between them. This makes it possible to see which countries are doing what, with what technologies, from which companies, and for which use cases. In short, AIMID aims to deliver a single, verifiable picture of global AI‑military integration—a data‑driven “ground truth.”

Among other benefits, the database seeks to help allies and partners pool knowledge about testing, evaluation, verification, and validation—supporting innovation and interoperability while avoiding costly duplication of technical efforts. It might also help reduce tension between global rivals by providing empirical reference points, reducing dangerous misperceptions, and ensuring that states’ security policies are anchored in evidence rather than ambiguity. For anyone concerned with responsible AI‑military development—governments, businesses, NGOs, academics, or average citizens—AIMID serves as a unique resource.

But defense leaders worldwide should go beyond merely recognizing that data fragmentation is hindering responsible military‑AI progress. They should next fund and adopt the tools necessary to fully understand the data and intelligence‑gathering side of this new ecosystem. While organizations like AIMID cannot fully solve the fundamental problem of data fragmentation, they represent some of the best resources available.

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