DH^CH / Chiara Capulli

Chiara Capulli is a postdoctoral fellow in the Lise Meitner Group at the Bibliotheca Hertziana – MPI and an associate researcher at the Kunsthistorisches Institut in Florence. She earned her PhD in History of Art from the University of Cambridge in 2024, with research focused on the impact of the 1529 guasto on Florence’s artistic and architectural heritage. Her work bridges early modern art history and digital methodologies, with experience in 3D modeling, historical data-mapping, and digital training. She has contributed to initiatives such as the Getty-funded Florence 4D project and has taught digital art history at institutions including Cambridge, Exeter, and Düsseldorf. At the KHI, Chiara is involved in research on the cultural impact of historical earthquakes in Central Italy and collaborates on digital reinterpretations of archival sources related to heritage damage in L’Aquila.

Title of Talk: Bias by Absence: Mapping Loss and the Promise of NLP in Post-Disaster Urban Histories
Abstract: This presentation explores the structural biases involved in reconstructing the urban, devotional, and architectural history of L’Aquila after the 1703 earthquake—a city repeatedly transformed by disaster, political intervention, and erasure. Drawing on an ongoing digital mapping project, I do not present a finished implementation of AI, but rather open up a set of methodological and ethical challenges related to fractured records and asymmetrical data. The project georeferences the 1753 Vandi map of L’Aquila to visualize sacred and civic structures marked as “ruined” or “disappeared.” These ruins speak not only to seismic destruction but to longer patterns of neglect, relocation, or instrumental reuse—particularly during the Fascist-era demolitions of the 1930s. In this context, bias emerges not just from what is recorded, but from what remains unsaid: saints without altars, artworks without provenance, churches with only partial footprints. I am especially interested in whether Natural Language Processing might help trace suppressed or dispersed information across difficult historical sources. Texts like Raffaele Colapietra’s unsystematic but rich writings resist conventional database approaches. NLP might extract spatial and temporal cues to support relational analysis—yet such tools also risk imposing reductive assumptions or flattening cultural nuance. Rather than proposing a solution, this case study invites discussion: how might AI be used critically to surface archival silences without reinforcing them? I seek feedback on the possibilities and limits of NLP in this context, advocating for a reflective engagement with data gaps, epistemic loss, and the politics of archival survival.

• Bias by Absence: Mapping Loss and the Promise of NLP in Post-Disaster Urban Histories