Short Abstract

The growing ubiquity of AI in society is currently being met with the scholarly formation of Critical AI Studies. However, what is the “critical” and the “AI” of Critical AI Studies? This panel invites contributors to explore these questions empirically, theoretically, reflectively and artistically.

Description

The ongoing hype around Artificial Intelligence (AI), often driven by the tech industry, continues to produce enchanted stories about AI's capabilities and its societal relevance (Campolo & Crawford, 2021). Consequently, formulating generative critiques that can both demystify such accounts and challenge current approaches to AI is increasingly needed. Critical AI Studies is an interdisciplinary ‘field in formation’ (Raley & Rhee, 2023), which offers a response to this need by aiming to better understand, critique and provide alternatives to the current regimes of AI development and implementation – from dataset production to model development, evaluation and deployment, as well as social, political and institutional contexts that shape them. Recent years have seen a variety of approaches to critically examining AI, such as ethnographic ‘lab studies’, reverse engineering or red teaming, technography, controversy mappings, artistic research, critical readings of computer science papers and historical genealogies of particular techniques used in AI. Yet, as an emerging field, Critical AI Studies has already been criticised for failing to question the ‘thingness’ of AI (Suchman, 2023), and there have been calls for more ‘critical’ methods (Offert & Dhaliwal, 2025). So, how do we ensure that we do not reproduce uncontroversial accounts of AI (Suchman, 2023) and that our critique does not run out of theoretical-methodological steam (Latour, 2004)? In this panel, we wish to pursue these questions and invite contributors to submit critical empirical studies of AI grounded in both established and more experimental/innovative methodologies, conceptual frameworks, and artistic practices, along with reflection pieces that dissect what we understand by ‘criticality’ and ‘AI’ in these efforts. STS scholarship is particularly well-placed to participate in such critical endeavours not only through in-depth empirical investigations of AI as a scientific field and situated practice, but also by activating these insights through acts of intervention (Downey & Zuiderent-Jerak, 2021).

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