Deep Learning and Digital Humanities

Progresses in convolutional neural networks are currently pushing the boundaries of machine learning. In just a couple of years, image and text analyses have reached levels of performance that open new avenues for finding patterns in large-scale digital archives and data fluxes. For this reason, Deep Learning is likely to soon change the entire Digital Humanities landscape, being at the core of a new family of search engines. In the coming years, we expect to see the invention of new tools based on Deep Learning networks that could be applied to fields as different as art history, literary studies, history, archaeology, musicology, and more.

However, the lack of transparency of convolutional neural networks also raises a number of epistemological issues. Indeed, the generalization of tools that perform extremely well but lack explicitness in their inner functioning can be problematic. What will be the impact of Deep Learning algorithms on scholarship? Will it be possible for scholars to train machines instead of programming them? Will convolutional networks be at the basis of new research methodologies? Will they open to a new kind of hermeneutics?

At a more societal level, to what extent can Digital Humanities research help to assess the increasing role of Deep Learning algorithms in everyday digital interactions? Can we detect when Deep Learning algorithms perform censorship or surveillance services? Can we add an ethical dimension to their functioning, avoiding for instance the use of particular discriminating features in their decisions? Can we use other algorithms to map and make explicit the functioning of Deep Learning networks?

This Research Topic welcomes all contributions that deal with Deep Learning applications to Cultural Heritage, Image, Textual and Musical scholarship, and Digital Humanities in general, or that question the societal and cultural impacts of the rapid rise of this technology.