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Chapter 3 looks at the entanglement of AI with systemic social structures. It starts with the way AI poaches its legitimacy from science. This authority acts as cover for AI’s role in increasing neoliberal precarity and austerity, spreading the social logic of financialization into everyday life. The chapter examines AI’s enrolment by institutions of the state, especially welfare systems, and the way it amplifies thoughtlessness and administrative violence. It closes with AI’s operation as a technology of racialization, its similarities to genetic determinism, and the way it becomes a candidate mechanism for a modern race science.
Chapter 7 lays out the way an anti-fascist approach to AI moves from resistance to restructuring. It shows, by example, why an anti-fascist approach must be both decolonial and feminist. The anti-fascist goal of structural renewal is discussed in terms of socially useful production, solidarity economies and the centrality of commons, such that optimization is replaced by commonization. Chapter 7 closes by outlining a new apparatus, one that resonates with a renewed vision of the social. While it may or may not use advanced computation, a new apparatus will support a transition to social autonomy. In place of AI as we know it, the recursive horizontality of a new apparatus is open and adaptive. Rather than trying to ‘solve’ anything, it helps to sustain care under radically changing conditions.
This chapter looks at the surprising brittleness of AI and the way its reliance on proxies and shortcuts haunts its social application. It critiques the post-hoc attempts to ‘fix’ AI’s damaging effects through ethics, regulation or human intervention, and focuses on the way AI not only produces discrimination but intensifies existing structural injustice. The chapter closes by looking at the way the performative character of AI produces the very subjects of its judgements, and at the inherently backward-looking nature of its solutionism. The result is deemed to be a recipe for ‘AI Realism’.
The introduction starts by grounding AI, for the purposes of the book, as the computational methods of deep learning and the associated institutions and ideologies. It sets out the reasons for resisting AI that are covered in Chapters 1 to 4, from its brittle solutionism and structural violence to the resulting scarcification and necropolitics. The chapter introduces the idea of an anti-fascist approach to AI as a necessary response to the contemporary combination of neural networks, reactionary politics and crisis conditions. It closes by outlining the path to overcoming existing AI, which is discussed further in Chapters 5 to 7, via feminist epistemologies, mutual aid, people’s councils and commons-based structural renewal.
This chapter looks at the impacts of AI under conditions of social crisis. It describes the way AI acts as an algorithmic shock doctrine, becoming an apparatus for producing states of exception. The effects of the COVID-19 pandemic prefigure the algorithmic distribution of life chances, whereAI acts as a necropolitical technology. The chapter explores the origins of AI’s mathematical optimizations in the history of eugenics, and its legacy in the search for superior intelligence. It identifies the steps by which AI may become, through ultrarationalism and neoreaction, a part of a fascistic politics. Chapter 4 closes by looking at AI’s potential enrolment in fascistic responses to the climate crisis.
This chapter delves into the actual mechanics of machine learning. It emphasizes the algorithmic operations of optimizsation as well as the dependencies on data. Moving on to neural networks, it examines the internal transformations of the data, for example through backpropagation, and the paradoxical pairing of predictive accuracy with opacity. The chapter highlights the materiality of AI and its consequences in terms of both carbon emissions and the centraliszation of control. It closes by focusing on the poorly paid and invisible workforce that underpins AI, and the continuous thread of anti-workerism that connects the origins of computing (Babbage) to contemporary applications of AI (Amazon).
Chapter 6 takes the ethics of Chapter 5 and turns them into political tactics through the principles of mutual aid and solidarity. Algorithmic boundaries and enclosures are challenged by a commitment to commonality. The struggles of workers ‘above’ and ‘below’ the algorithm are put in relation to the potential of the workers’ council, a directly democratic form of organizing that is a starting point for structural renewal. This is extended to the idea of the people’s council as a self-constituting struggle against abstract segregation. Resistance to AI is compared to the historical movement of Luddism and the need for community constraint of harmful technology. The overall approach is anti-fascist, in that it is both an early recognition of the intensity of the threat and a defence of the space for emancipation.
Chapter 5 sets out a standpoint for resisting AI based on mutuality and care. It draws from feminist and post-normal science as a way to challenge exclusionary claims to authoritative knowledge. The chapter applies feminist new materialism to interrupt AI’s configuration of reality, and to counter its operations of separation with a perspective that is fundamentally relational. It establishes a form of critical pedagogy that can be used to ‘learn against the machine’ and recover the potential of prefigurative politics, of ‘the possible against the probable’. Ultimately, the chapter recomposes the question of AI as a matter of care that directs attention to the effects of boundaries and exclusions and to the neglect of our interdependence.
Artificial Intelligence (AI) is everywhere, yet it causes damage to society in ways that can’t be fixed. Instead of helping to address our current crises, AI causes divisions that limit people’s life chances, and even suggests fascistic solutions to social problems. This book provides an analysis of AI’s deep learning technology and its political effects and traces the ways that it resonates with contemporary political and social currents, from global austerity to the rise of the far right.
Dan McQuillan calls for us to resist AI as we know it and restructure it by prioritising the common good over algorithmic optimisation. He sets out an anti-fascist approach to AI that replaces exclusions with caring, proposes people’s councils as a way to restructure AI through mutual aid and outlines new mechanisms that would adapt to changing times by supporting collective freedom.
Academically rigorous, yet accessible to a socially engaged readership, this unique book will be of interest to all who wish to challenge the social logic of AI by reasserting the importance of the common good.
The chapter examines the effects of changes in research and innovation (R&I) funding on gendered practices (Korvajärvi, 2012), gender (in)equalities and the formation of women’s career paths in R&I. The context is Finland, where R&I and its funding expanded in the 1990s and 2000s and then declined significantly between 2008/09 and 2015. Drawing on Dorothy Smith’s (2005) institutional ethnography, the chapter analyzes the career histories of Finnish women (N=30) working in research in and outside of academia, in the multidisciplinary field of health technology. Most of the interviewees had lived through both the expansion period and the cuts that emerged in R&I funding in the post-2000s. Many had found opportunities for research work during the period of plentiful funding and had started successful research careers, and then faced the hardening competition of declining resources with experiences of gender inequality and even direct gender discrimination. Gendered (male/female dominated or mixed gender) work communities shaped these inequalities and especially for women researchers with a ‘reproductive body’ (Pecis, 2016) who were treated unequally and even excluded. We argue that significant changes in R&I funding intensify gender inequalities and affect the career paths of women in R&I.
