Last autumn I shared the call for proposals for a conference on Digital Humanities Today: Critical Inquiry with and about the Digital at King’s College London.

I’m involved in organising a session on “critical AI practices” at the conference at 0930-1100 on Friday 26th June. This is co-convened with Tobias Blanke (Amsterdam), Liliana Bounegru (KCL), Sal Hagen (Amsterdam), Noortje Marres (Warwick), Sabine Niederer (Amsterdam), Donato Ricci (Sciences Po) and Daniela van Geenen (Siegen), together with other colleagues at the Public Data Lab.

The full programme can be found here and further details about our session are copied below.

As well as catching up with many friends and colleagues, I’m also excited that I’ll finally be able to meet my namesake Jonathan W. Gray who is giving a talk on “Rebuilding a Commons: From Black Twitter to Bluesky’s Blackademics” in the 1130-1300 that same Friday. We’ve been in touch for a while about the many Jonathan Grays - but it is the first time we’ll be meeting IRL. ✨🌌

Critical AI practices

What are the prospects of “critical AI practices” grounded in digital arts and humanities research? This panel proposal draws together critical AI, data and Internet researchers to explore how the notion of critical technical practice might inspire contemporary critical and creative engagements with machine-learning technologies.

Originating in ‘endogenous critique’ of symbolic AI, critical technical practices (CTP) have been characterised as those which have “one foot planted in the craft work of design and the other foot planted in the reflexive work of critique” (Agre, 1997). This notion has been adopted and adapted by many fields and communities of practice – re-envisaging critique in relational terms (van Geenen, van Es & Gray, 2023). How might CTP be re-activated, re-equipped and pluralised to address challenges posed by current paradigms of deep-learning and data-intensive AI and their consequences?

In keeping with the theme of this event, critical AI practices can be envisaged as those which support critical inquiry both with and about AI technologies. They explore how capacities of machine learning may be reoriented in light of critical research on AI and its social, cultural, economic and ecological consequences – from biased datasets to polluting data centres to extractivist economies. How might critiques of AI grounded in decolonial, feminist and Indigenous scholarship inform not only refusing AI, but ways of engaging with it differently?

This panel examines different approaches to critical AI practices through a review of a range of practices and projects in these areas, as well as underpinning theories – drawing on research from across the Public Data Lab research network and the Deep Culture project at the University of Amsterdam. It aims to support those interested in critical AI practices to gather and learn from each other – re-imagining and recomposing the epistemic and societal possibilities of AI beyond the platforms, infrastructures and logics which currently predominate.

The panel is proposed by Tobias Blanke (Professor of AI and Humanities; University of Amsterdam), Liliana Bounegru (Senior Lecturer in Digital Media, Culture and Society, King’s College London), Jonathan Gray (Reader in Critical Infrastructure Studies, King’s College London), Sal Hagen (Postdoctoral researcher in Deep Learning and Digital Methods, University of Amsterdam), Noortje Marres (Professor in Science, Technology and Society, University of Warwick), Sabine Niederer (Professor of Visual Methodologies, Amsterdam University of Applied Sciences), Donato Ricci (Head of Design Research, médialab, Sciences Po, Paris), Daniela van Geenen (PhD Candidate, University of Siegen), together with colleagues from the Public Data Lab.

References

• Agre, P. E. (1997). Toward a Critical Technical Practice: Lessons Learned in Trying to Reform AI. In Social Science, Technical Systems, and Cooperative Work. Psychology Press.

• van Geenen, D., van Es, K., & Gray, J. W. (2023). Pluralising critical technical practice. Convergence, 30(1), 7–28. https://doi.org/10.1177/13548565231192105