Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. 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. 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. Quantitative methods are commonly used to study various phenomena, including different types of Artificial Intelligence AI. However, scholars from various disciplines are recognizing the limitations of this approach Rahwan et al, ; Adadi and Berrada, ; Afnan et al. Importantly, Marda and Narayan argue that the uncritical and positivist use of quantitative methods fails to consider the contextual factors. This lack of understanding contributes to the blackboxing of AI, leading to inaccurate perceptions of these technologies.
As a result, people may have either excessive or insufficient trust in AI. Moreover, organizations and institutions can exploit this blackboxing to evade legal responsibility Sartori and Theodorou, Increasingly, ethnography is recognized as a method that could help scientists investigate AI in a more holistic and critical manner. In fact, the call for ethnography as a method to study AI has grown louder over the past decade Suchman, ; Forsythe, ; Seaver, ; Marda and Narayan, ; Sartori and Theodorou, We agree with these and other authors that ethnography, a qualitative method par excellence, can offer insights into technology as a sociotechnical phenomenon Hess, ; van Voorst, ; Ahlin, Additionally, rather than defining variables in advance and testing hypotheses, ethnographic fieldwork allows for surprising and unexpected discoveries.
Researchers can conduct open-ended, in-depth interviews and engage in participant observation of how individuals and institutions design, create, and use technologies Pols, As the enthusiasm for ethnographically studying the dynamic and urgent field of algorithmic society grows, it becomes even more important to do justice to the depth of this methodology. This is an ongoing point of consideration as technologies also influence and reshape the norms of ethnographic research Ahlin and Li, In this commentary, we first highlight three key aspects of an ethnography of AI that will contribute to the success of ethnographic studies: committed fieldwork, establishing trustful relationships in the field, and developing an attentiveness for subtle and nuanced data.
