Sensitivity and Access: Unlocking the Colonial Visual Archive with Machine Learning

In recent decades, archival institutions have digitized an enormous quantity of material under the rubric of open access, including from colonial archives. However, much of the most sensitive material from these collections remains undigitized or difficult to discover and use. More recently, a critical reconsideration of open digital access has also taken place, particularly when it comes to sensitive material from the colonial archive. Collectively, this has created a situation in which the colonial photography archive risks becoming overly sanitized as well as difficult to navigate and analyze. In this article, we propose that critical and transparent multimodal artificial intelligence (AI) offers a way to improve access to colonial archives for researchers and the public, without losing sight of the need for ethical approaches to sensitive visual materials. The EyCon (Early Conflict Photography and Visual AI) project assembled a large database of sensitive visual materials from colonial conflicts and developed experimental multi-modal computer vision tools with which to analyze it. Though this tool has not yet been applied at scale or quantitatively compared with other approaches, we are able to propose modes of inquiry for other researchers to explore as they create new research tools. On a more hypothetical or theoretical level, we consider how the use of computational tools to facilitate access to and analysis of sensitive historical materials is compatible with or even beneficial for more ethical approaches to such materials. We conclude with several promising areas for critically integrating AI into the digital colonial archive, while also expanding on some limitations of such techniques.