Working group Critical Digital Humanities

This work closely connects to my role on the board of directors of the German Association for Digital Humanities (Digital Humanities im deutschsprachigen Raum, DHd).

At DHd 2023, the working group organised a workshop on data feminism (missing reference), the results of which were subsequently reported in blog posts published in both English (Borek et al., 2023) and German (Borek et al., 2023). At the same conference, we co-organised a panel (Gengnagel et al., 2023) and also co-organised a panel at the international DH2023 conference in Graz that same year (Borek et al., 2023). I further represented the working group in a panel on failure in DH (Wuttke et al., 2023) that was reported on here (Lang, 2024).

Our longer-term engagement with data feminism and (the gender) data gap(s) culminated in the publication of a ZfdG working paper on data feminism in 2026 (Lang & Suárez Cronauer, 2026). Complementing this work, we published a more focused study on the gender data gap in the Computational Humanities Research journal: In the article, “Dataset Audits for Mitigating Data Gaps,” we propose the dataset audit as a practical strategy for identifying and addressing data gaps while discussing the gender data gap more broadly (missing reference). Preliminary research into the topic had examined dataset documentation practices (Lang, 2025).

Following this extended focus on data feminism, the current projects of the working group are increasingly turning towards the study of data work and its influence on processes of knowledge production (Gengnagel & Lang, 2026). This is closely connected to fields such as critical AI studies, which increasingly recognise data and data work as central sources of bias and unethical outcomes in computational systems.

Consequently, my own research has also expanded to include critical AI. This includes articulating critical concerns regarding the use of large language models in digital and computational humanities (Lang, 2026), as well as addressing issues such as carbon reporting and the environmental impact of computational research (missing reference). Through this work, I am increasingly developing what I would like to call a critical computational humanities that that articulates principles for good scholarly practice in contexts that use digital, computational and AI methods.

At DHd2026, I represented the working group in a panel about the dark sides of DH (Arnold et al., 2026).

Recently, I have developed my early reflections on computational humanities in the form of a blog post (Lang, 2020) into a piece investigating invisibilised labour in Computational Humanities contexts through the lenses of data work and the invisible technician discourse (Lang, 2027).

All in all, critical DH/CH is an area that I particularly enjoy, and it will form the basis of my next book project after the completion of my habilitation. The book will investigate biases and distortions in datasets. As scholarship increasingly relies on datasets that are inherently fragmented (especially in historical disciplines, but also in contemporary contexts), the project examines the consequences of these limitations and their implications for knowledge production. In it, I aim to articulate a disciplinary ethics beyond compliance, providing a framework for critically engaging with the epistemic and ethical challenges posed by data-driven research.