Nuclear Non-Proliferation, Satellite Images, AI, and Risk
Creators: name: Frank, Rebecca
Publisher: name: Zenodo
Year: 2025
Subjects: Curation infrastructure: Movements in Open and Citizen Science and trust in digital curation
Sustainability and strategy: AI for data re-use -- automated data collection, cleaning, analysis, and visualisation
Working with challenging data: Sensitive data, retrieval and secure curation
satellite image data
risk
expertise
open source intelligence
IDCC25

description: Satellite images are one type of data used by analysts who monitor states to verify compliance with nuclear non-proliferation and arms control agreements (“citizen-based monitoring & verification”) (e.g., Al-Sayed, 2022; Zegart, 2022). The community of analysts who engage in this work use data from a variety of sources, including satellites as well as other publicly available information, often referred to as open source intelligence or OSINT. The work of obtaining and analyzing satellite image data with the express goal of identifying activities such as the development of weapons facilities requires the work of highly skilled analysts (e.g., “Trainspotting, with Nukes; Open-Source Intelligence,” 2021).

This poster will present preliminary results from a July 2024 workshop involving 14 satellite image analysis experts. Participants engaged in a gallery walk with six research team-designed posters, featuring titles, images, and discussion prompts. In small rotating groups, they annotated posters with sticky notes and discussed prompts aloud, with conversations audio-recorded and transcribed for analysis. This study received approval from the author’s Institutional Review Board.

I explore here the following research questions:

1. How and in what ways do experts in satellite image analysis use machine learning and/or artificial intelligence (“ML/AI”) in their work?

2. How and in what ways do those experts understand current and/or future applications of ML/AI in their work?

3. How do those experts understand risks associated with their work in light of recent developments in ML/AI?

Preliminary findings suggest that experts in satellite image analysis for citizen-based monitoring and verification understand that ML/AI has a useful role to play in the management and processing of digital information, but that they tend not to find it directly useful for their current image analysis work. Participants were able to discuss a range of potential future applications for ML/AI, but with the caveat that the inherently risky nature of their work would demand greater transparency than is currently available.

The work of analyzing OSINT, and satellite images in particular, is one with high stakes (e.g., Diaz-Maurin, 2022; Niemeyer, 2009). People with expertise in image analysis who work in this space have seen rapid advancements in the technologies and data sources available in recent years (e.g., Pabian, 2016; Parcak, 2019; Parks, 2005; Solodov, 2007; Zegart, 2022).

Despite both the advancements in technology and the proliferation of available sources and types of OSINT, the citizen-based monitoring and verification community is one that places high value on the experience and judgment of experts. This is a community of data users for whom trust in the source of data, in the work of expert data users, and in the ability of publics to understand and believe in the work of those experts is crucial at all stages of work. This poster examines the ways in which those experts understand and think about the current and future impact of ML/AI on their high-stakes work.