Regimes of Representability

We are asking these questions at a time when AI is increasingly used to generate personalized knowledge e.g., when replacing the lists of results of classic search engines with short summaries or extractive snapshots of the knowledge that is available (for you) on the internet (sic!). Scholars have discussed ad nauseam how both the data sets used for training machine learning algorithms and the processes through which these models “learn” result in skewed representations of reality — because they reproduce biases in data and iterate ways of knowing that render many aspects of reality invisible or unrepresentable.

Yet, none of these discussions and critiques have prevented those technologies framed as AI that have come to govern what we see, remember, and believe to know. In this CML in-conversation event, we will therefore discuss how AI has, despite all its known shortcomings, established credibility, reliability, and relatability — as e.g., a more ethical tool of representing violence, a more personal and intimate respondent, or a more balanced consensus-builder.

Thao Phan will present her and Fabian Offert’s research on nonhuman witnessing through AI. Through the discussion of two projects, Exhibit.ai and Calculating Empires, they interrogate the paradox of AI: that a technology that is itself unrepresentable is tasked and sold as a more ethical way of documenting and representing the unrepresentable (e.g., genocidal violence). They analyse the political propositions of both these works, and in particular, their suggestion to work with/through AI to negotiate the representability of forms of political violence today.

Ranjodh Singh Dhaliwal will discuss the rise of AI-branded computational photography in our smartphones, particularly as it aims to correct for the racism of previous regimes, especially that enabled by the disparate rendering of skin color in film photography. Through a closer look at the hardware, software, and media representations of Google’s Real Tone technology, he shows how our images and our infrastructures today mis-represent the possibility of aesthetic and technical solutions to sociopoliticoeconomic problems.

In a follow-up conversation with CML contributors Qingyi Ren and Johannes Bruder, Thao and Ranjodh will also discuss strategies of resistance against the new regimes of representability that AI technologies have introduced. How can we reclaim agency in the process of knowledge generation as bots & algorithms are—more or less subtly—changing the make-up of reality?