POSTPONED: Machine Learning Political Orders (Louise Amoore)

Description

The transformation from rules-based algorithms to deep learning models has been a condition of possibility for the undoing of rules-based social and political orders, from the Brexit challenges to EU integration to the austerity politics and digitalization of welfare states and the pandemic NHS. Where rules-based computation and decision was critical to the formation of post-war politics, what happens when the machine learning function displaces it? The processes of machine learning extracts features from data, clustering attributes, and mapping optimal functions. Computer science has become a political force because of its claim that any existing function can be approximated by a deep neural network, so that the algorithmic political arrangement becomes one in which all political problems can be figured as 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?”;
• “what is the representation of crime data that optimizes the output of urban policing in this district of London?”.

It is for this reason that it is insufficient to merely say that automated technologies or machine learning systems disrupt our social order, or undercut our existing bodies of rights. It is more significant, even, than this disruptive force. For it is itself a mode of politics that arranges the orderings of public space, adjudicates what a claimable right could be, discriminates the bodies of those on whom it is enacted. What we are witnessing with machine learning politics may be a transformation from algorithmic rules conceived to tame a turbulent, divided, and capricious world, to the productive generation of turbulence and division from which algorithmic functions are derived.

 

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’.