Politics of Algorithmic Life Workshop
19-20 May 2026, Leverhulme Centre for Algorithmic Life, Durham
Speakers include:
Lauren Wilcox on War beyond the human
Ben Jacobsen on Politics of synthetic data
Jenny Edkins on Certainty and the fantasy of closure
Sign up here: Politics of Algorithmic Life Workshop – Leverhulme Centre for Algorithmic Life – 19&20 May – Fill in form
The Politics of Algorithmic Life workshop is free to attend. Travel and accommodation bursaries are available for PhD students and early career researchers in precarious positions or without access to research funds.
What does the rise of artificial intelligence and machine learning algorithms mean for politics? At first glance it may seem self-evident that AI is transforming politics and has consequences that we would call political. When machine learning algorithms are used to select targets for military strikes or to generate probabilities for immigration enforcement, the technologies are tangibly implicated in the exercise of sovereign political power and violence. Beneath these spectacular political effects, are algorithmic technologies registering or even constituting new political rationalities? What underlying logics link the disparate deployment of these techniques in warfare, at the border, in an election, or a political protest? Contemporary machine learning algorithms are not only used in politics but are themselves techniques of government in the broadest sense – enabling novel ways of assembling populations, making inferences, and even expressing political will.
When Ian Hacking (2006) noted that the human sciences historically “create kinds of people that in a certain sense did not exist before” he had in mind the “making up of people” and societies associated with statistical knowledges. This world-making power of statistical models defined a specific relationship of technology to politics in biopolitical modernity, making possible the rationalization of politics and life through bureaucratic modes of administration (Agar, 2003), governed by the rule of law (Daston, 2022). Intuitively, algorithms, understood as rule-based computational procedures, should intensify such calculative rationalities of government. But in our current conjuncture, we see longstanding relationships between expertise and liberal government breaking down. The emboldenment of far-right and fascist movements; the violent alliances between tech companies and racist and misogynist political agendas; the colonial foreclosure of resistances and counter-movements – these have flourished in an age defined by “the AI state”.
How can we characterize the distinctiveness of this (geo)political environment in relation to the algorithmic technologies that underpin it? Algorithms and especially AI have become both a means of governing populations and increasingly a more autonomous end of government itself. More familiar logics of security justify state interventions into microchip supply chains, while the spectre of total economic transformation produces distinctive political alliances between frontier research labs, the financiers underwriting their massive capital expenditure, and states themselves. In what ways, then, do the concepts and spatial arrangements of algorithms remake politics in their own image—and vice versa?
Contemporary generative AI and “task agnostic” machine learning models, for instance, actively subvert inherited forms of domain expertise and expert specialization so central to rational administration in favour of a multi-purpose (and multi-modal data) model that travels across disparate political fields, seeking generalised solutions to di icult problems. As machine learning algorithms are increasingly trained on novel forms of synthetic data (an algorithm-to-algorithm political relation), even the concept of governing via data involves a world made by algorithms for use by other algorithms.
What happens to the political categories of race, territory, border, nation when these are differently configured with machine learning spatial imaginaries? How might the distinctive distributions modelled by AI and machine learning even narrow or foreclose the possibility of political futures?
