Editors’ Summary: This post details the investigation into social media accounts that depict Black women through AI-generated characters in order to identify solutions to this quickly-growing problem. The joint investigation identified over 100 social media accounts running out of 34 different countries. These accounts all used entirely AI-generated media, depicted Black women, and were sexual […]
Editor’s Summary: This post provides a detailed explanation of how transformers work. Transformers in this context refers to a tool for sequence transduction (converting one sequence of symbols to another) an essential tool for natural language processing. The author provides a step by step discussion of how transformers work in terms of language, including many […]
Editors’ Summary: This post considers how generative AI has broken down the barrier to entry for doing digital history. The author argues that while AI is bad at doing history, it can enable historians to do good digital history. He acknowledges that in some instances code generated by AI immediately becomes technical debt, especially for […]
Editors’ Summary: This article re-emphasizes a sense of Humanities criticality in DH from the perspective of Oral History and how that becomes even more crucial in the Global South. The author highlights the construction of an oral history archive on Partition. This position leans heavily on the works of Sumallya Mukhopadhyay, who redirects focus from […]
Editors’ Summary: This project combines oral history interviews, digital storytelling, and GIS to map Sapphic areas of New York City. The author begins by visualizing all of the bars in NYC, and identifying the four bars that are explicitly lesbian bars. The author encourages the reader to move beyond the idea of bars as the […]
Editors’ Summary: This article argues that bespoke code used in digital humanities research should be treated as a core part of scholarly output rather than treated as invisible technical labor. It introduces defactoring, a method of close reading and restructuring, to reveal a project’s underlying computational narrative. The authors demonstrate this approach by unpacking the […]
Editor’s Summary: This post responds to another viral essay “Academics Need to Wake Up on AI” that argued that academia is in denial about how good Claude Code is at producing the 6,000–8,000 word journal article. The author argues that the peer-reviewed journal article as the primary unit of academic production is likely dead, and […]
Editors’ Summary: In this post, the author shows how the rise of AI has made the critical thinking component of using code in DH even more essential. He details his initial explorations using Claude Code to build DH web apps for use in the classroom. This post argues that the old tools of Digital Humanities […]
Editor’s Summary: This article considers the bold claim that politicians from different parties really “all sound the same”. It trains an AI model on 20+ years of Finnish parliamentary speeches and compares its guesses about affiliation with that of 438 human readers. It turns out that the system is ‘better’ at telling parties apart. Humans […]
Editors’ Summary: This post shares some practical ways college instructors can design courses so students are not completely dependent on AI. It highlights grading systems that reward process over product, moving from writing to problem solving to finally reaching a solution. It empowers the students into realising the value of thinking for themselves instead of […]
