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 — particularly photographs depicting colonial violence — 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. Photographic material presents a particularly tense point in the debate over access and sensitivity, largely due to the longstanding notion that it is a “transparent” medium, one that bears an exact trace of the moment in which it is made. For this reason, photography is commonly perceived or experienced as a more immediate carrier of emotions – including painful or negative emotions – than other kinds of documents or representations. Enormous quantities of photographic material have been digitized without sufficient contextual metadata. What metadata exists was created by colonial institutions themselves, and thus may not respond to the questions researchers want to ask. At the same time, much of the most sensitive material remains hidden due in large part to increasing awareness of ethical concerns. For these reasons, the digitally available colonial photography archive risks becoming overly sanitized as well as difficult to navigate and analyze. In this article, we ask how machine learning (ML) might redress this problem. Specifically, how might a set of ML-informed tools improve access and navigation for this sensitive digital archive? We suggest that critical and transparent multimodal ML 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. We tested such techniques on our data (a very large corpus of colonial conflict photographs collected from various archives in France and the UK), in order to determine whether it might be possible to design an interface that provided better results than non-ML augmented digital databases and currently-available off-the-shelf ML tools available from Amazon and Google. While our reflections remain largely hypothetical and these ideas were not carried out at scale, they are nonetheless suggestive of a number of paths forward on using ML and computer vision on sensitive visual materials. This article explains the archival problems presented by digitized photographs from the colonial period, and then examines potential ways that ML-augmented computational approaches might make access both more robust and more sensitive.