In this blog post, I would like to argue for critical computational humanities. Computational humanities has many great advantages and promises. One of them is definitely the possibility of unlocking historical sources, using dazzling, cutting-edge new methods to look at really old sources and uncover what’s hidden within them. That’s certainly one of the fascinations the field has always held for me. Computational methods help us see things that would otherwise remain invisible. They allow us to analyse material at scales that would otherwise not be feasible, or so our arguments typically go. Cite as: Sarah Lang, Towards Critical Computational Humanities, in: LaTeX Ninja’ing and the Digital Humanities (blog), October 8th 2026. https://latex-ninja.com/2026/10/08/towards-critical-computational-humanities/ But computation comes with strings attached. And not just computation itself, but also the methods associated with computational humanities and the whole tradition that has developed around them. Recently, in the introduction to our special issue on Computational Methods for Histories of Alchemy and Chemistry, we argued
AI
Machine Learning for the Humanities: A very short introduction and a not-so-short reflection
Machine Learning is one of those hot topics at the moment. It’s even starting to become a really hot topic in the Humanities and, of course, also in the DH. But Humanities and Machine Learning are not the most obvious combination for many reasons. Tutorials on how to run machine learning algorithms on your data are starting to pop up in large quantities, even for the DH. But I find it problematic that they often just use those methods, just show you those few lines of code to type in and that’s it. Frameworks have made sure that ML algorithms are easy to use. They actually have a super-low entry level programming-wise thanks to all those libraries. But the actual thing about ML is that you need to understand it or it’s good for nothing. (Ok, I admit there are some uses which are pretty straightforward and don’t need to be fully understood by users, such as Deep Learning powered
