Embracing Absence in Cultural Collections

Ellen Charlesworth, M.A.

This project explores how algorithmic mediations reflect and exacerbate absences in cultural collections. Many datasets – in both foundation models and digital art history projects – are unrepresentative or incomplete. Through omission, they often reinforce the western canon or gender and racial stereotypes. Yet, while many projects focus on correcting or minimising this bias, it is impossible to eradicate entirely. What can we do to nuance or utilise this inevitable feature?

Drawing from the growing school of thought that conceptualises biases not as a fault but as a feature of datasets, I will explore how we can improve and nuance quantitative analysis by treating absence not as a lack of information, but as a different type of data. The research is structured around three interrelated research questions: (Q1) In what ways is absence perceptible in cultural collections data and the models trained on them? (Q2) What processes and biases – collecting practices, historic priorities, and research methods – do these absences reflect? (Q3) And how can we better communicate and visualise these absences within digital art history?