We investigate the impact of computer vision models, a prominent artificial intelligence tool, on critical knowledge infrastructure, using the case of Google search engines. We answer the following research question: How do search results for Google Images compare internationally with those for Google Search, and how can these results be explained by changes in Google's knowledge infrastructure? To answer this question, we carry out four steps: (1) Theorise the relationship between web epistemology, calculative technology and issue configuration, illustrating the dynamics of critical knowledge infrastructures on the web; (2) provide a potted history of Google's use of computer vision in search; (3) undertake the first international comparison of search results from Google Search and Google Images; (4) analyse the visual content of search results from Google Images. Adopting a novel research design combining a suite of quanti-quali digital methods including visual content analysis, social semiotics and computer vision network analysis, we analyse six countries’ search results related to environmental change (climate change, biodiversity loss). We present two key findings. First, Google Images search results contain fewer authoritative sources than Google Search across all countries. Second, Google Images results constitute a narrow, homogenised visual repertoire across all countries. This constitutes a transformation in Google's web epistemology from ranking-by-authority to ranking-by-similarity, driven by a shift in calculative technology from web links (Google Search) to computer vision (Google Images). Our framework and findings open up new questions regarding the impact of computer vision on public access to knowledge in increasingly image-saturated digital societies.
Pearce et al. (Fri,) studied this question.