• Lise Jaillant
• Katherine Aske

• archives
• digitization
• machine learning
• cultural heritage
• access

• medical images
• digital archives
• artificial intelligence
• responsible access

Through a series of interviews with 10 archivists, librarians, and researchers based in the UK and US, the authors show that improved access to medical illustrations is essential to produce new knowledge in the humanities and medical research, as well as to bridge the gap between historical and modern understandings of the human body.

• Lise Jaillant

• archives
• digitization
• glam
• machine learning

• artificial intelligence

This Special Issue focuses on the theme of  Using Visual AI Applied to Digital  Archives. It is an invitation to work collaboratively, across disciplines and sectors, to address challenges associated with AI and fully embrace the potentialities of technology to make visual archives more accessible in an ethical way.

• Jonathan Dentler
• Lise Jaillant
• Daniel Foliard
• Julien Schuh

• visual art
• machine learning
• archives

• artificial intelligence
• computer vision
• colonial archives
• sensitive images
• visual culture
• open access
• sensitivity
• critical artificial intelligence
• multimodal artificial intelligence

Can AI help confront colonial photographic archives? Check out this article exploring how semi-automation can augment our capacity to produce knowledge using digital visual archives of contested pasts.

• Adéla Hamplová
• Avital Romach
• Josef Pavlíček
• Arnošt Veselý
• Martin Čejka
• David Franc
• Shai Gordin

• machine learning
• digitization

• cuneiform
• convolutional neural networks
• artificial intelligence
• OCR
• object detection

An innovative approach towards cuneiform OCR of identifying strokes instead of signs offers insight into the challenges and methodologies of quantitative epigraphy.

• Andrew Prescott

• philosophy
• race
• dh
• cultural criticism

• Artificial Intelligence
• Bias
• Machine Learning
• Big Data

This article surveys the ways in which issues of race and gender bias emerge in projects involving the use of predictive analytics, big data and artificial intelligence (AI).