#AoIR2024 Industry

Session Chair: 
 
Presentation 1
 
UTILITY OF INDUSTRY- PROVIDED SOCIAL MEDIA DATA FOR RESEARCH PURPOSES: A SYSTEMATIC AUDIT OF TIKTOK’S API FOR RESEARCHERS
Jessica Yarin Robinson⁽¹⁾, George Pearson⁽²⁾, Nathan Silver⁽²⁾, Mona Azadi⁽²⁾, Jennifer Kreslake⁽²⁾
1: University of Oslo, Norway; 2: Truth Initiative, Washington, D.C.
 
Decisions to restrict data access to major platforms like Twitter and Reddit have recently led to scholarly discussions of a “post-API” world, in which researchers can no longer depend on company-sanctioned access to powerful platforms (Bruns, 2019; Freelon, 2018; Tromble, 2021). Yet TikTok appeared to counter the trend with the release of a Research API in 2023. The API was designed to aid researchers studying TikTok, reducing the need to rely on external open-source tools (e.g. PykTok), where the scope of data may be limited.
Scholars granted access to the API can search for videos and users, and retrieve metadata on viewership, engagement with, and circulation of videos. TikTok allows researchers to retrieve up to 100,000 records per day. Such data could help provide insight into patterns of communication on one of the world’s fastest growing social media platforms, and one that is increasingly used by young people for social interaction, information seeking, and political speech (Cervi et al., 2023; Schellewald, 2021; Song et al., 2021).
Following the tradition of previous digital data quality audits (Pfeffer et al., 2023; Tromble et al., 2017), we perform an audit of the TikTok Research API, with an interest in helping researchers evaluate the utility and quality of the API. We conclude that research based on this data source may lack scientific validity, particularly if it relies on video metadata (likes, shares, views, comments). However, we also discuss potential uses of the Research API for archival searches.
 
 
Presentation 2
 
A study of industry influence in the field of AI research
Glen Berman⁽¹⁾, Kate Williams⁽²⁾, Eliel Cohen⁽³⁾
1: Australian National University, Canberra, ACT, Australia; 2: University of Melbourne, Melbourne, Victoria, Australia; 3: King's College London, Strand, United Kingdom
 
In this paper, we explore how AI researchers, situated within university-based research networks, mobilise and resist industry interests. The research question to which this paper is addressed is: how do university-based academics in the field of AI experience and mediate industry influence in their research? We answer this question through semi-structured interviews with research-focused academics (n = 90) affiliated with university-based AI research networks. We find that national research funders and university leadership incentivise and facilitate industry investments in AI research. And, we demonstrate how AI researchers mobilise this interest to pursue their own research goals, whilst also—at times—subordinating their research goals to the interests of industry. We highlight how AI researchers internalise the commercial logics of technology firms, which become mirrored in researchers' orientation towards generalisable and scalable research outputs that can move between many application domains and local contexts. We argue that university-based AI research networks primarily operate as mediators between industry, government, and university actors, and highlight the role national research investment strategies play in creating an enabling environment for industry influence of AI research.
 
 
Presentation 3
 
Research GenAI: Situating Generative AI In The Scholarly Economy
Peta Mitchell⁽¹⁾, Michelle Riedlinger⁽¹⁾, Jake Goldenfein⁽²⁾, Aaron Snoswell⁽¹⁾, Jean Burgess⁽¹⁾
1: Queensland University of Technology, Australia; 2: University of Melbourne, Australia
 
This paper charts the emergence of a distinct category of research-dedicated GenAI platforms, which we term Research GenAI or RGAI. These platforms are explicitly marketed to a cross-disciplinary academic audience, promising to automate research discovery and writing tasks, such as identifying/summarising published research, writing literature reviews, conducting data analysis, and synthesising findings. RGAI platforms (e.g., Consensus, Elicit, Research Rabbit, Scholarcy, Scite, SciSpace) are rapidly being adopted, in a context of experimentation, uncertainty, and controversy.
We define the contours of Research GenAI by mapping the history and development of RGAI platforms and developing a preliminary typology of RGAI. We situate RGAI platforms within the scholarly economy and ongoing processes of platformisation and automation of academic work. We make a case for the need to understand RGAI platforms as complex sociotechnical systems that intersect with social, ethical, institutional, and legal questions, and demonstrate this approach through an STS-informed walkthrough of two notable RGAI platforms: Consensus and Elicit. In this presentation we present our findings generated from these walkthroughs and explore the implications of the technologies for the academic publishing industry.
 
 
Presentation 4
 
Unpacking Expertise in the Privacy Tech Industry
Rohan Grover
University of Southern California, United States of America
 
Corporate and civil society actors have assembled an emergent privacy tech industry. This nascent industry seeks to help companies comply with data protection laws and consists of a network of startups, consultants, investors, platforms, and domain experts. This study draws on ethnographic fieldwork to ask: what is the role and the nature of expertise in constructing the privacy tech industry? I describe three findings. First, my findings demonstrate that the privacy tech industry constitutes a networked arena of relations structured by a neat partitioning of professional expertise, especially across technical, legal, and operational domains. Second, my findings demonstrate that focal actors in the privacy tech industry assert values and interpretations that merit public debate. Third, I find that the exclusion of lay expertise perpetuates the technocratic structural relations of a surveillance economy. These findings demonstrate that expertise in the privacy tech industry constitutes a key analytic that mediates relations among professionals groups, between people and technical systems, and between professional experts and the public. I conclude by describing how the privacy tech industry would benefit from drawing from alternative sites of expertise, including embodied, subjective, affective expertise from the people who stand to benefit most from data protection and data privacy laws.