Datasets for 'From authority to similarity: How Google transformed its knowledge infrastructure using computer vision'

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?<b> </b>To answer this question, we carry out four steps: 1) theorise the relationship between web epistemology, calculative technology, platform vernacular 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)<b> </b>undertake<b> </b>the first international comparison of search results from Google Search with Google Images; 4) analyse the visual content of search results from Google Images. Using quanti-quali digital methods including visual content analysis, social semiotics and computer vision network analysis, we analyse search results related to environmental change across six countries, with 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 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 findings and theoretical model open up new questions regarding the impact of computer vision on the public availability of knowledge in our increasingly image-saturated digital societies.