Artificial intelligence (AI) and machine learning (ML) are becoming increasingly significant areas of research for scholars in science and technology studies (STS) and media studies. In March 2020, Waymo, Google/Alphabet’s autonomous vehicle project, introduced the ‘Open Dataset Virtual Challenge’, an annual competition leveraging their Waymo Open Dataset. This freely accessible dataset comprises annotated autonomous vehicle data from their own Waymo vehicles. Yearly, Waymo has continued to host iterations of this challenge, inviting teams of computer scientists to tackle evolving machine learning (...) and vision problems using Google's data and tools. This article analyses these challenges, situating them within the context of the ‘Grand Challenges’ of artificial intelligence (AI), which aimed to foster accountable and commercially viable advancements in the late 1980s. Through two exploratory workshops, we adopted a ‘technographic’ approach to examine the pivotal role of challenges in the development and political economy of AI. Serving as an organising principle for the AI innovation ecosystem, the challenge connects companies and external collaborators, driving advancements in specific machine vision domains. By exploring six key themes—interface methods, incrementalism, metrics, AI vernacular, applied domains, and competitive advantages—the article illustrates the role of these challenges in shaping AI research and development. By unpacking the dynamic interaction between data, computation, and labour, these challenges serve as catalysts propelling advancements towards self-driving technologies. The study reveals how challenges have historically and presently shaped the evolving landscape of self-driving and AI technologies. (shrink)

Social media platforms’ digital advertising revenues depend considerably on partnerships. Business partnerships are endemic and essential to the business of platforms, yet their role remains relatively underexplored in the literature on platformisation and platform power. This article considers the significance of partnerships in the social media ecosystem to better understand how industry platforms, and the infrastructure they build, mediate and shape platform power and governance. We argue that partners contribute to ‘platformisation’ through their collective development of business-to-business platform infrastructures. Specifically, (...) we examine how partners have integrated social media platforms with what we call the audience economy – an exceptionally complex global and interconnected marketplace of intermediaries involved in the creation, commodification, analysis, and circulation of data audiences for purposes including but not limited to digital advertising and marketing. We determined which relationships are involved, which are exclusive or shared, and identified key ecosystem partners. Further, we found that partners build and integrate extensive infrastructures for data-sourcing and media distribution, surfacing infrastructural and strategic sources and locations, or ‘nodes’, of power in this ecosystem. The empirical findings thus highlight the significance of partnerships and partner integrations and draw attention to the powerful industry players and intermediaries that remain largely invisible. (shrink)

This study explores Big Data practices at Facebook through an investigation of the role of commensuration or ‘the transformation of different qualities into a common metric’ in the structuration of analysis and interaction with a major online social media platform. It proposes a conceptual framework and demonstrates the empirical potential of a pragmatic approach based on reading published materials and available documentation. Facebook’s Data Warehousing and Analytics Infrastructure serves as an illustrative example to begin tracing out and describe data assemblages (...) in more detail. In being attentive to the motivations, drivers and challenges engineers face when dealing with Big Data, it is argued that their solutions can enable and support but also constrain specific analytical and transactional capabilities or data flows between various devices and actors. The analysis thus moves beyond methodological critiques of the utility of Big Data that lack empirical support and specificity. It is further argued that analytics not just describe but also actively participate in the enactment of social worlds, thereby opening possibilities for new markets or market segments to arise. Online sociality accounts for a model of the social that makes it visible and measurable qua markets inviting data recontextualisation and the creation of value along multiple axes. Contra Facebook’s claim to make the web more ‘social’, an investigation of commensuration brings to the fore the question how the social is accounted for in the first place. (shrink)