Insightful analysis of historical sources at scales beyond human
  capabilities using unsupervised Machine Learning and XAI
Oliver Eberle Jochen Büttner Hassan El-Hajj Grégoire Montavon Klaus-Robert Müller Matteo Valleriani
Historical materials are abundant. Yet, piecing together how human knowledge
has evolved and spread both diachronically and synchronically remains a
challenge that can so far only be very selectively addressed. The vast volume
of materials precludes comprehensive studies, given the restricted number of
human specialists. However, as large amounts of historical materials are now
available in digital form there is a promising opportunity for AI-assisted
historical analysis. In this work, we take a pivotal step towards analyzing
vast historical corpora by employing innovative machine learning (ML)
techniques, enabling in-depth historical insights on a grand scale. Our study
centers on the evolution of knowledge within the `Sacrobosco Collection' -- a
digitized collection of 359 early modern printed editions of textbooks on
astronomy used at European universities between 1472 and 1650 -- roughly 76,000
pages, many of which contain astronomic, computational tables. An ML based
analysis of these tables helps to unveil important facets of the
spatio-temporal evolution of knowledge and innovation in the field of
mathematical astronomy in the period, as taught at European universities.