Speakers
Description
Explainable AI for handwriting identification has so far mostly stopped at the heatmap: pixel-level relevance maps that scholars can inspect but rarely act upon. This talk traces a path from diagnosis to action to discovery. We first present a validated, model-agnostic explainability framework for medieval handwriting classifiers, assessed for faithfulness, stability, and cross-model agreement, and benchmarked against expert paleographic judgement across four Latin manuscript datasets. We then show that these faithful explanations can be repurposed as data-curation signals: a data-centric retraining strategy that uses positively-attributed evidence to construct improved second-stage training sets, systematically outperforming random sample selection on Vatican manuscript datasets. Finally, we outline how this foundation feeds into an ongoing research programme that aims to reframe handwriting identification as an explainable, open-set discovery process at the scale of the Codices Latini Antiquiores — moving from patch-level saliency to segment-based explanations, aggregated graphic signatures, and natural-language descriptions grounded in a paleographic ontology. Together, these strands argue for a shift in how explainability is used in digital paleography: not as an afterthought to classification, but as an instrument that can curate training data and, ultimately, support historical interpretation itself.