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Challenges as Catalysts:
How Waymo’s Open Dataset Challenges Shape AI Development

📂Challenges in AI and Self-Driving 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 labor, these challenges serve as catalysts propelling advancements toward self-driving technologies. The study reveals how challenges have historically and presently shaped the evolving landscape of self-driving and AI technologies.

📋 ✍Cite

📋Cite (APA) Hind, S., van der Vlist, F. N., & Kanderske, M. (Accepted/In Press). Challenges as Catalysts: How Waymo’s Open Dataset Challenges Shape AI Development. AI & Society: Knowledge, Culture and Communication. Springer Nature. DOI: 10.1007/s00146-024-01927-x.
🔗Link (DOI)

Kind Journal Article; Original Research Article
Author S. Hind; F. N. van der Vlist; M. Kanderske
Publication Date 2024, April 17 [first published online]
Journal AI & Society: Knowledge, Culture and Communication (AI&S)
Pages (17)
Publisher Springer Nature (London, United Kingdom)
Identifier 10.1007/s00146-024-01927-x [self]; 1435-5655 [part of]; 0951-5666 [part of]; 262513311 [funded by]
License CC BY 4.0


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