Machine Learning Political Cultures

Public lecture by Louise Amoore, professor of Human Geography in the Department of Geography, Durham University, UK.

The processes of machine learning are infiltrating our political culture so that fundamental questions of society and governance are reconfigured. In this lecture Louise Amoore examines how computer science has become a political and cultural force, so that all socio-political problems become figured as potential machine learning problems.

Consider how a political question becomes refigured in and through the propositions of machine learning: “what is the optimal representation of all the input immigration data to achieve this target of limited immigration?”; “what is the best representation of all human mobility data to achieve the target of limiting Covid-19 transmission?”.

It is for this reason that it is insufficient to merely say that automated machine learning systems disrupt our social order, or undercut our existing bodies of rights. What is taking place is more significant, even, than this disruptive force. Machine learning is itself a mode of political culture that arranges the orderings of public space, adjudicates what a claimable right could be, discriminates the bodies of those on whom it is enacted.

The transformation from twentieth century rules based algorithms to deep learning models has thus simultaneously been a condition of possibility for the undoing of rules based social and political orders, from Brexit to the digitalization of welfare states and the pandemic NHS. Where rules-based computation and decision was critical to the formation of post-war political culture, and to the formation of welfare states in 20th century, what happens when the machine learning function displaces it? What we are witnessing with machine learning political culture may be a transformation from algorithmic rules conceived to tame a turbulent and divided world, to the productive generation of turbulence and division from which algorithms learn to thrive.


Louise Amoore is professor of Human Geography in the Department of Geography, Durham University, UK. She works on the politics of algorithms, the geopolitics of technology, biometric futures, and the ethics of machine learning systems. Her book, Cloud Ethics: Algorithms and the Attributes of Ourselves and Others (Duke University Press, 2020), examines algorithms as ethico-political entities that are entangled with the data attributes of people, and locates the ethics of algorithms in the partiality and opacity that haunt both human and algorithmic decisions.  In her earlier work, including her book The Politics of Possibility: Risk and Security Beyond Probability (Duke University Press, 2013), Louise traces how probability and statistical calculation are reframed through algorithmic possibilities and forms of calculation. Louise’s current research is funded by a five year ERC Advanced grant, ‘Algorithmic Societies’.