Cultural and media studies articles from across Nature Portfolio

Recent years have seen an increase in calls for ethnography as a method to study Artificial Intelligence (AI). Scholars from diverse backgrounds have been encouraged to move beyond quantitative methods and embrace qualitative methods, particularly ethnography. As anthropologists of data and AI, we appreciate the growing recognition of qualitative methods. However, we emphasize the importance of grounding ethnography in specific ways of engaging with one’s field site for this method to be valuable. Without this grounding, research outcomes on AI may become distorted. In this commentary, we highlight three key aspects of the ethnographic method that require special attention to conduct robust ethnographic studies of AI: committed fieldwork (even if the fieldwork period is short), trusting relationships between researchers and participants, and, importantly, attentiveness to subtle, ambiguous, or absent-present data. This last aspect is often overlooked but is crucial in ethnography. By sharing examples from our own and other researchers’ ethnographic fieldwork, we showcase the significance of conducting ethnography with careful attention to such data and shed light on the challenges one might encounter in AI research.

Most of what I know of street art and graffiti, I learned by walking the streets of Bogotá, Colombia. Like most people, I initially did not notice that my hometown was saturated with murals and pieces, as well as hastily executed stencils, tags and throw-ups. But just like a gestalt image, once you shift your gaze towards the street, you start seeing them, and then there is no turning back. My fascination with these strange letters and images, done mostly by anonymous artists, led me to start thinking through the tensions and contradictions embedded in our public spaces and eventually to writing my master’s thesis on Bogotá’s street art and graffiti culture.

Borrowing the format of public competitions from engineering and computer science, a new type of challenge in 2023 tested real-world AI applications with legal assessments based on the EU AI Act.

In Japan, people express gratitude towards technology and this helps them to achieve balance. Yet, dominant narratives teach us that anthropomorphizing artificial intelligence (AI) is not healthy. Our attitudes towards AI should not be bult upon overarching universal models, argues Shoko Suzuki.

Online health communities can provide valuable social support in China, writes Qingpeng Zhang. He argues that they offer benefits that artificial intelligence cannot match.