My abstract for the workshop on ‘Teaching Anti-Fascism Today: Developing and Shaping Practices’ to be held at the Institute of Languages, Cultures and Societies, School of Advanced Study, University of London. An anti-fascist teaching practice is also anti-AI. The intervention I propose is first and foremost aimed at participants in the workshop. It will argue that AI is a vector for fascistic currents in education and society, and that an anti-fascist teaching practice means resisting AI both as a pedagogic tool and more broadly. The presentation will emphasise that AI is fascistic not only because of the fusion between MAGA and Silicon Valley accelerationism, but due to AI’s core operations and infrastructural dependencies. Moreover, AI is being mobilised as a direct attack on teaching and education as such, with the net effect of eliminating spaces of critical thought. Where AI comes to predominate, inside education or in society, it extends a technologically-mediated metapolitics that is fundamentally misogynist and eugenicist. ...

My speaker notes from the ‘Resisting Big Tech Empires’ conference, Sat 25th April 2026. The opening plenary (pictured) was with Anita Gurumurthy of IT for Change and Nick Dearden of Global Justice Now. The afternoon panel was with investigative journalist Nafeez Ahmed. Opening plenary Q1: “What do we mean when we talk about Big Tech Empires?” I’m not sure there are Big Tech empires, but there are definitely Big Tech corporations in the middle of a world system collapse. The so-called rules-based order was always a cover story, but now it’s over anyway. Power is shifting; it’s an interregnum, a time between, and AI is a morbid symptom of it. ...

new preprint: ‘AI, Decomputing and the Interregnum’ cite as: McQuillan, D. (2026). AI, Decomputing and the Interregnum. Zenodo. https://doi.org/10.5281/zenodo.18908529 abstract: This paper treats AI as diagnostic for the deeper changes taking place in the existing order of things. It uses AI’s alignment with both the political economy and with the dualisms that underpin it, including race, gender and anthropocentrism, to highlight the nihilistic character of the current restructuring. AI’s scaling and accelerationism are taken as examples of the wider tactics being invoked by hegemonic power to maintain control under changing conditions. From this perspective, the massive build-out of data centres isn’t simply a seizure of energy resources but a manifestation of an aggressive and misogynist technopolitics. The paper argues that a liberal push for digital sovereignty doesn’t interrupt these dynamics but plays into the hands of emerging technofascism. It proposes instead the prefigurative tactic of ‘decomputing’, which draws on degrowth, deautomatisation and a convivial approach to technology. It explores decomputing as a means to mitigate both material and relational harms and as a decisive turn towards infrastructuring the common good. The paper concludes that AI is the contradiction that reveals many others, not least the gap between claims to legitimacy and the actuality of destructive violence, and proposes an alternative technopolitics of reciprocity that prioritises care and sustainability.

This is the abstract for my Rogers Chair Public Lecture for Western University on 20th March 2026 at 5pm GMT, hosted by the Starling Centre for Just Technologies and Just Societies. URL for the talk tbc. Let us begin from the contradiction that is AI. What does it mean that a technology which is functionally incapable of delivering on its claims continues to be the site of massive financial and strategic investment? This question can be fruitfully answered by treating AI as a diagnostic whose failings shed a light on underlying structural transformations. AI is a fake solution to real crises, especially the breakdown of the neoliberal world order and the unsettling of patriarchal subjectivity. Like all solutionism, it actually amplifies the very problems it’s intended to solve. The particular pathologies of AI, especially scaling and accelerationism, are symptoms of the actual restructuring taking place in relation to energy politics and increasing authoritarianism. ...

Video, Speaker notes & slides from the panel on ‘Resisting GenAI & Big Tech in Higher Education’, an event co-organised with the Climate Justice Universities Union (CJUU). The panel: Christoph Becker (U of Toronto, CA), Mary Finley-Brook (U of Richmond, USA), Dan McQuillan (Goldsmiths U of London, UK), Sinéad Sheehan (University of Galway, Ireland) Jennie Stephens (National University of Ireland Maynooth, IE), and Paul Lachapelle (U of Montana, USA). Chair: Amy Woodson-Boulton (Loyola Marymount U, USA). ...

My opening statement to the Irish Parliament (Oireachtas) joint Committee on Artificial Intelligence. The theme of the session was ‘AI, Truth and Democracy’ I would like to thank the Committee for the invitation to participate in this discussion about AI, Truth and Democracy. AI AI is both a set of technologies, such as neural networks and transformer models, and a range of rhetorical claims. The technology and the claims are only loosely connected. I will argue that AI undermines the ideals of truth and democracy. Truth AI has an adversarial relation with truth. The core of AI’s calculations are correlations not causal relations, so it’s outputs are plausible rather than factual. AI’s pattern recognition is, therefore, a form of computational conspiracy theory, and its outputs are disinformation even when they appear to be accurate. AI’s internal opacity and its inability to parse social complexity make it impossible to remove bias and errors. While a belief in AI’s superior powers persists, its claims to truth will retain authority while harming the most marginalised. Even the engineers who build AI can’t explain what’s going on inside, so reliable regulation is a non-starter. At the same time, the efforts to make AI more reliable actually make it more effective at selecting preferred, and usually “non-woke”, versions of truth. The claims that AI will solve everything from climate change to infectious disease deflect attention from the uncomfortable truths of our current moment. This hubris is driving an investment bubble that diverts vast sums from real social needs. Democracy It’s increasingly clear that AI is precaritising rather than productive; it can’t replace people but it makes their conditions more vulnerable. AI is extending forms of austerity prevalent since the crash of 2008 while preparing a new financial crash of its own, creating conditions which are corrosive to democracy. In addition, AI is anti-democratic in terms of institutional and regulatory capture. We are currently witnessing the EU walking back the flagship AI Act in the face of pressure from Trump and Big Tech. Peter Thiel, founder of Palantir and patron of J. D. Vance, is on a lecture tour saying attempts to regulate AI are the work of the Antichrist. Meanwhile, the example of DOGE demonstrated AI’s effectiveness as a form of authoritarian cyberattack on centralised institutions. More broadly, AI is toxic to democracy via its impact on education and young people. Large Language Models are sold as learning accelerators but substitute slop for critical thinking. They are becoming the first port of call for everything from essays to relationship advice. AI undermines the replenishment of a citizenry with the capacity for independent thought. In its systemic effects, AI will fail to solve problems, cause collateral damage, and will benefit reactionary politics. Recommendations I suggest that the committee avoid misleading responses to this state of affairs, such as the idea of AI sovereignty. AI should be considered harmful to whatever polity is hosting it. The committee should at least be clear with itself what it’s endorsing when it endorses AI. At best, the alleged benefits to healthcare and so on really amount to algorithmic Thatcherism. A more likely outcome is that widespread AI adoption will strengthen the far right. I urge the committee to see AI as a symptom rather than a cause, and to use it as a diagnostic for the underlying problems of a system that needs restructuring for the benefit of people and planet. Having said that, AI is an actor in its own right and one that will intensify real world problems like energy costs, unemployment and militarisation. Therefore, I also encourage the committee to place worker and community collectives at the heart of decision-making about AI, with a clear power of veto. This should also apply to expanding the number and/or scale of energy-hungry and recolonising data centres. Decision-making around AI should prioritise alternative solutions that reduce the overall dependence on computation and elevate direct social relationships.

An abstract for the European University of Technology’s (EUt+) webinar series on digital transformation. REGISTRATION FORM The EU’s dubious vision of economic growth that could be digitally decoupled from carbon emissions has resulted in a deeply reactionary technopolitics. While it was never really the case that innovative tech efficiencies would magic up unlimited growth without climate consequences, the mask has really slipped with the advent of AI. Massive institutional and financial buy-in is failing to conceal AI’s character as an already-broken technology trying to fix a broken system. While AI’s imaginaries are far-right-friendly and accelerationist, the material consequences include a re-centring of high energy consumption as a sign of progress. ...

A talk for the Low Carbon and Sustainable Computing (LOCOS) seminar series at the University of Glasgow. The video of the talk. This talk will attempt to extend the idea of low carbon computing by calling for collective decision-making around the development and deployment of computational technologies. While progress can be made on both energy demands and embodied carbon, these are currently swamped by the accelerationist effects of technologies such as AI. Not only is our current political-economy unsustainable, as the IPCC points out, but the attempt to fix it through a fusion of AI and increased authoritarianism are only making things worse. ...

This is the text of a talk given as part of the lecture series “Unstitching Datafication”, organised by the Media for Cooperation research centre at the University of Siegen. It’s very much a work-in-progress, and I’m interested in any comments or feedback: feel free to email me at resistingai@gmail.com or find me on social media. intro I would like to thank Christoph and the Media of Cooperation research centre for inviting me to be part of the Unstitching Datafication series. I’ll try to honour the series by using terms like ‘seams’ and ‘unstitching’ in my talk. ...

I have a new piece out in the Berliner Gazette: ‘Resisting the Techno-Fascist Takeover: Are We Ready for Decomputing?

This is the text of a seminar given at the Goldsmiths Centre for Philosophy and Critical Thought on June 11th 2025 I would like to thank the Centre for Philosophy and Critical Thought for inviting me to give this seminar. This talk is titled ‘The role of the University is to resist AI’, and takes as its text Ivan Illich’s ‘Tools for Conviviality’. AI’s impact on higher education come primarily from historical forces, not from its claim to be sci-fi tech from the future. Society can’t throw up its hands in shock as students outsource their thinking to simulation machines when fifty years of neoliberalism has masticated education into something homogenised, metricised and machinic. Meanwhile, so-called Ed Tech has claimed for decades that learning is informational rather than relational and ripe for technical disruption. ...

I wrote this Q&A to help me prepare for a TV interview about AI and disability. I tried to include concrete examples and I steered clear of theory (not my usual approach!). The questions were what I imagined might come up, the answers are my attempt to challenge those assumptions. The answers are disjointed because they’re a collection of talking points for a conversation. They boil down quite a lot of background research, and if I get the time I’ll add links to the sources. I’m posting them here in case they’re helpful for anyone else who wants to challenge the disability-washing of AI. I’d be interested in any feedback and comments, or other examples I could add to the list, so feel free to drop me a line at resistingai@gmail.com. ...

Thanks to POLITICO Pro Morning Technology UK for asking me to respond their weekly Q&A for the Friday 13th(!) edition. As they put it in the newsletter intro, “AI academic Dan McQuillan has some… strong feelings about the U.K.’s AI strategy”. Q&A Dan McQuillan, senior lecturer in critical AI at Goldsmiths, University of London and the author of “Resisting AI: An Anti-fascist Approach to Artificial Intelligence,” gave MTUK a radical take on the U.K.’s AI strategy, saying the government is over-committing on the technology. ...

This is the draft abstract for my research seminar at the Goldsmiths Centre for Philosophy and Critical Thought (CPCT), as part of the 2024-2025 series ‘On Truth and Lies in the Extramoral University’. The hybrid seminar will be on Wednesday 11th June 2025 4:00-6:00pm UK time in RHB room 138 and on Zoom. Online registration is available via the seminar session page. Drawing on Illich’s ‘Tools for Conviviality’, this talk will argue that an important role for the contemporary university is to resist AI. The university as a space for the pursuit of knowledge and the development of independent thought has long been undermined by neoliberal restructuring and the ambitions of the Ed Tech industry. So-called generative AI has added computational shock and awe to the assault on criticality, both inside and outside higher education, despite the gulf between the rhetoric and the actual capacities of its computational operations. Such is the synergy between AI’s dissimulations and emerging political currents that AI will become embedded in all aspects of students’ lives at university and afterwards, preempting and foreclosing diverse futures. It’s vital to develop alternatives to AI’s optimised nihilism and to sustain the joyful knowledge that nothing is inevitable and other worlds are still possible. The talk will ask what Illich has to teach us about an approach to technology that prioritises creativity and autonomy, how we can bolster academic inquiry through technical inquiry, workers’ inquiry and struggle inquiry, and whether the future of higher education should enrol lecturers and students in a process of collective decomputing.

I was fortunate to have the chance to debate with staff from Anthropic in an event at the Tate Gallery. Under discussion were the ethical and social impacts of Claude and other LLMs, especially in the context of the project at the Tate and Anthropic’s push into education. The panel discussion was lively and wide-ranging. Here are the prompts I prepared for myself beforehand, in case they’re useful to anyone who wants to question the inevitability of AI in their university, educational institution or other setting. These questions are a boiled-down summary of extensive research and I have the receipts, as they say. ...

This is a placeholder for the open access chapter on ‘AI Infrastructures, Total Mobilisation and Decomputing’ which will be coming out soon in the volume ‘AI Infrastructures and Sustainability’ edited by Anne Mollen, Fieke Jansen, Sigrid Kannengießer, Julia Velkova and published by Palgrave Macmillan. When it comes out I’ll link to the chapter and update the timestamp. “This chapter examines the broad impact of AI infrastructures on the possibility of sustainable social and ecological relations. It problematises these infrastructures not only in terms of energy demand but as a degradation of labour and social relations, and highlights the ways in which AI itself fails to deliver on its claims. Identifying the driving principle of ‘scale’ as central to these problems, it positions AI infrastructures with the wider frame of growth ideology. Addressing the contradictions between AI’s harmful fallibilities and the vast investments of financial and political capital in its infrastructures, the chapter offers Ernst Jünger’s concept of ‘total mobilisation’ as a way to grasp the underlying, epochal dynamics. This framing also helps to explain the observable overlap between Silicon Valley’s own ideologies and the rise of far right political forces. The chapter proposes the idea of ‘decomputing’ as a counter to the cumulative harms of AI infrastructures. It develops the concept of decomputing in relation to ideas of degrowth and de-automatisation, and suggests some practical steps forward based on the collective adoption of convivial technologies.”

This is the full version of the article that was published in Computer Weekly on 14-1-25. Labour has published its ‘AI Opportunities Action Plan’ (The Plan). The Prime Minister is very bullish about The Plan, and peppers his foreword with muscular terms like growth, revolution, ambition, strength and innovation. In itself, The Plan is full of claims that AI is essential and inevitable, and urges the government to pour public money into the industry so as not to miss out. ...

I was interviewed by Diyar Saraçoğlu, translator of ‘Yapay Zekâya Direnmek’ (the Turkish edition of ‘Resisting AI’), for their series on The Political Construction of Artificial Intelligence, which gave me an opportunity to add some reflections on the current moment. Can artificial intelligence be seen not only as a technical development but also as an ideological project? How do you view the fundamental issues of AI from a historical perspective? I don’t think any development is ‘only’ technical. All technology is embedded in history, politics and our social imaginaries. It’s more helpful to think in terms of technopolitics, where the technical and political dimensions are intertwined like strands of DNA. AI in particular is an apparatus, a configuration of concepts, investments, policies, institutions and subjectivities that act in concert to produce a certain kind of end result. In the case of AI, the historical currents it’s channelling include eugenics and white supremacy. ...

This is the introduction to a longer article which has been published in Computer Weekly. “Labour has published its ‘AI Opportunities Action Plan’ (The Plan). The Prime Minister is very bullish about The Plan, and peppers his foreword with muscular terms like growth, revolution, ambition, strength and innovation. In itself, The Plan is full of claims that AI is essential and inevitable, and urges the government to pour public money into the industry so as not to miss out. In the style of tech entrepreneurs, The Plan likes to put ‘x’ after things, so investment must go up by 20x (meaning twenty times), the amount of compute AI requires has already gone up by 10,000x and so on. The Plan claims that Britain is already leading the world through the AI Safety Institute (of which more later) and infuses the usual AI hype with a nationalist vibe via terms like world leader, world-class, national champions and ‘Sovereign AI’. Above all, The Plan emphasises the need to scale. The significance of scale for AI and its technopolitical impacts will be explored below. ...

Abstract for a forthcoming chapter in an open access volume on ‘AI Infrastructures and Sustainability’, to be published by Palgrave The advent of generative AI has thrust aspects of AI infrastructure into popular awareness, not least its seemingly insatiable demand for energy. At the same time, various insider ideologies circulating in Silicon Valley have aligned with the far right. This chapter argues that these developments are intimately connected. The scaling of AI’s energy, labour and computing infrastructures not only increases environmental and social harms but becomes the ’total mobilisation’ of material and human resources, justified by an openly supremacist worldview. ...

My piece in the Scottish left Review: A Just Transition Means Resisting AI

My article on Bellacaledonia, a follow-up to my participation in a panel on AI at Holyrood: Betting on AI will take Scotland backwards not forwards

A talk given at the The 2nd Ecology of AI Workshop in June 2024 One of the concepts I introduce in this talk is the idea of ‘decomputing’, as a 50:50 hybrid of decolonial and degrowth approaches. “Decomputing challenges the expansionism of scale that AI brings to its technical form, to its environmental demands and to its social impacts. This expansionism is AI’s version of ‘growth’, and it’s empty metrics echo GDP in the ways they conceal the underlying destructiveness. Decomputing takes the idea of ‘computing within limits’ to refer not only to the scale of computational machinery but to limits of extractive and colonial logics, limits to a biosphere’s ability to recover, limits to our Western knowledge systems and limits to tech solutionism. ...

(Abstract for a chapter which will appear in ‘The Handbook of Critical Studies of Artificial Intelligence and Education’, published by Edward Elgar Publishing in 2025) This chapter will argue that education is vulnerable to capture by AI because it has already been made machinic. Neoliberalism has shaped education to be standardised, optimised and scalable, a trajectory that exactly matches the values encoded in machine learning[1]. Moreover, the adoption of AI will widen the gap between education and the cultivation of critical thinking. Rather than simply delivering the ‘banking model’[2], AI-driven education enacts speculation based on “the social logic of the derivative”[3]: both teachers and learners will be further enmeshed in systems of predicted future value. At the same time, thanks to generative AI, the very tools that are embraced as a creative renewal of pedagogy[4] will enforce the hyper-normativity sedimented inside transformer and diffusion models. ...

At the same time that UK political parties vie in their condemnation of the Post Office scandal, they unite in their promotion of AI as the answer to tricky social problems. This means that, in effect, they are arguing for more of the same; for more occasions where computing and bureaucracy combine to mangle the lives ordinary people, but scaled by AI in ways that make Horizon’s harms look like small beer. ...

It’s tempting to see the recent UK AI Safety Summit as a damp squib, preempted by an Executive Order on AI from the Whitehouse and roundly criticised by civil society for excluding everyone but tech execs. Unfortunately, none of the current debate gets to the heart of the matter: AI is already a flop, and we are being hoodwinked by a mixture of corporate and ideological agendas that will wreck public services and deepen social divisions. ...

Some quick notes on the EU’s AI Act. These were written the morning after so I might update them as more details emerge. The whole thing is premised on a risk-based approach(1) This is a departure from GDPR, which is rights-based with actionable rights Therefore it’s a huge victory for industry(2) It’s basically a product safety regulation that regulates putting AI on the market The intention is to promote the uptake of AI without restraining ‘innovation’(3) Any actual red lines were [dumped a long time ago](EU guidelines: Ethics washing made in Europe) The ’negotiation theatre’ was based on how to regulate gen AI (‘foundation models’) and on national security carve-outs People focusing on foundation models were the usual AI suspects People pushing back on biometrics etc were civil society & rights groups The weird references in the reports to numbers like ‘10~23’ refer to the classification of large models based on flops(4) Most of the contents of the Act amount to some form of self-regulation, with added EU bureaucracy on top(5) Ironically, an epistemology of ‘risk’ is one of the key things that makes predictive AI so harmful ↩︎ ...

Executive summary Large language models contain foundational flaws which mean they are unable to live up to the hype and make it likely that the current bubble will burst. They will continue to require vast amounts of invisibilised labour to produce, but will not result in any form of artificial general intelligence (AGI). The greatest risk is that large language models act as a form of ‘shock doctrine’, where the sense of world-changing urgency that accompanies them is used to transform social systems without democratic debate. ...

Opening remarks for the panel on ‘Political Interventions in Data and AI’ at #DataJustice2023 is a social movement to resist AI, because a real challenge to algorithmic violence requires structural change. AI isn’t sci-fi but a radical continuity of modernity, of bureaucracy, of austerity; of the anti-worker, anti-poor contempt that stretches from Charles Babbage to Jeff Bezos. Regulation and reform are undermined by the absence of a fair status quo, but also by applying half-solutions to the entanglement of the tech and the social. ...

Large language models (LLMs) like the GPT family learn the statistical structure of language by optimising their ability to predict missing words in sentences (as in ‘The cat sat on the [BLANK]’). Despite the impressive technical ju-jitsu of transformer models and the billions of parameters they learn, it’s still a computational guessing game. ChatGPT is, in technical terms, a ‘bullshit generator’. If a generated sentence makes sense to you, the reader, it means the mathematical model has made sufficiently good guess to pass your sense-making filter. The language model has no idea what it’s talking about because it has no idea about anything at all. It’s more of a bullshitter than the most egregious egoist you’ll ever meet, producing baseless assertions with unfailing confidence because that’s what it’s designed to do. It’s a bonus for the parent corporation when journalists and academics respond by generating acres of breathless coverage, which works as PR even when expressing concerns about the end of human creativity. ...

Chapter abstracts for ‘Resisting AI - An Anti-fascist Approach to Artificial Intelligence’ Available from Bristol University Press chapter 0 - introduction 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, introduces the idea of an anti-fascist approach to AI, and closes by outlining the path to overcoming existing AI that is taken in Chapters 5 to 7. ...

A talk given to the Mellon Sawyer Seminar on the History of AI, University of Cambridge, June 23rd 2021 It’s time to talk about the Ludding times. That’s how they talked about it, those who were there, when readying themselves for an insurrection a few years later. The Ludding times of 1811 to 1816, when communities in Nottinghamshire, Lancashire and the West Riding of Yorkshire rose up against the machines. The Ludding times, with all their similarities to our own times. What can we learn from the Luddites? What can we learn from the heft of a hammer and the idea that, as a later revolutionary said, the urge to destroy is also a creative urge? ...

The speakers notes for a 6.5 minute talk given at the Data Justice Lab in Cardiff, June 7th 2019. 1. not transformation but intensification The introduction of process automation and predictive analytics via machine learning is not a transformation, it’s an intensification. Machine learning and bureaucracy are both generalisable modes of rational ordering based on abstraction and deriving authority from claims to neutrality and objectivity. The justification for bureaucratic rationality is efficiency. Machine learning adds inferential governance in the name of optimisation. ...

A talk given at the launch of the ‘All Access AI’ network at Goldsmiths, Unversity of London 1st April 2019. This talk refers to a text developed for Propositions for Non-Fascist Living: Tentative and Urgent, published by BAK, basis voor actuele kunst and MIT Press (forthcoming November 2019). intro This talk is about some pressing issues with AI that don’t usually make the headlines, and why tackling those issues means developing an antifascist AI. ...

This talk was given at the conference ‘Rethinking the legacy of 1968: Left fields and the quest for common ground’ held at The Centre for Cultural Studies Research, University of East London on September 22nd 2018 http://rethinking1968.today/ There’s a definite resonance between the agitprop of ‘68 and social media. Participants in the UCU strike earlier this year, for example, experienced Twitter as a platform for both affective solidarity and practical self-organisation1. However, there is a different geneaology that speaks directly to our current condition; that of systems theory and cybernetics. What happens when the struggle in the streets takes place in the smart city of sensors and data? Perhaps the revolution will not be televised, but it will certainly be subject to algorithmic analysis. Let’s not forget that 1968 also saw the release of ‘2001: A Space Odyssey’ featuring the AI supercomputer HAL. ...

Voice as Biomarker ‘You sound a bit depressed’ we might say to a friend Not only because of what they say but how they say it Perhaps their speech is duller than usual, tailing off between words Lacking their usual lively intonation There are many ways to boil a voice down into data points Low-level spectral features, computed from snippets as short as twenty milliseconds 1 That quantify the dynamism of amplitude, frequency and energy And those longer range syllabic aspects that human ears are tuned to Such as pitch and intensity ...

The Manifesto on Algorithmic Humanitarianism was presented at the symposium on ‘Reimagining Digital Humanitarianism’, Goldsmiths, University of London, Feb 16th 2018. Download at SocArXiv intro humanitarian organisations will adopt ai because it seems able to answer questions at the heart of humanitarianism such as ‘who should we save?’ and ‘how can we be effective at scale?’ it resonates strongly with existing modes of humanitarian thinking and doing in particular the principles of neutrality and universality the way machine learning consumes big data and produces predictions suggests it can both grasp the enormity of the humanitarian challenge and provide a data-driven response but the nature of machine learning operations mean they will actually deepen some humanitarian problematics and introduce new ones of their own thinking about how to avoid this raises wider questions about emancipatory technics and what else needs to be in place to produce machine learning for the people maths there is no intelligence in artificial intelligence nor does it really learn, even though it’s technical name is machine learning it is simply mathematical minimisation like at school, fitting a straight line to a set of points you pick the line that minimises the differences overall machine learning does the same for complex patterns it fits input features to known outcomes by minimising a cost function the fit is a model that can be applied to new data to predict the outcome the most influential class of machine learning algorithms are neural networks which is what startups call ‘deep learning’ they use backpropagation: a minimisation algorithm that produces weights in different layers of neurons anything that can be reduced to numbers and tagged with an outcome can be used to train a model the equations don’t know or care if the numbers represent amazon sales or earthquake victims this banality of machine learning is also it’s power it’s a generalised numerical compression of questions that matter there is no comprehensions within the computation the patterns are correlation not causation the only intelligence comes in the same sense as military intelligence; that is, targeting but models produced by machine learning can be hard to reverse into human reasoning why did it pick this person as a bad parole risk? what does that pattern of weights in the 3rd layer represent? we can’t necessarily say. reasoning machine learning doesn’t just make decisions without giving reasons, it modifies our very idea of reason that is, it changes what is knowable and what is understood as real it operationalises the two-world metaphysics of neoplatonism that behind the world of the sensible is the world of the form or the idea. a belief in hidden layer of reality which is ontologically superior, expressed mathematically and apprehended by going against direct experience. machine learning is not just a method but a machinic philosophy what might this mean for the future field of humanitarian ai? it makes machine learning prone to what miranda fricker calls epistemic injustice she meant the social prejudice that undermines a speaker’s word but in this case it’s the calculations of data science that can end up counting more than testimony the production of opaque predictions with calculative authority will deepen the self-referential nature of the humanitarian field while providing a gloss of grounded and testable interventions testing against unused data will produce hard numbers for accuracy and error while making the reasoning behind them inaccessible to debate or questioning using neural networks will align with the output driven focus of the logframe while deepening the disconnect between outputs and wider values hannah arendt said many years ago that cycles of social reproduction have the character of automatism. the general threat of ai, in humanitarianism and elsewhere, is not the substitution of humans by machines but the computational extension of existing social automatism production of course the humanitarian field is not naive about the perils of datafication we all know machine learning could propagate discrimination because it learns from social data humanitarian institutions will be more careful than most to ensure all possible safeguards against biased training data but the deeper effect of machine learning is to produce new subjects and to act on them machine learning is performative, in the sense that reiterative statements produce the phenomena they regulate humanitarian ai will optimise the impact of limited resources applied to nearly limitless need by constructing populations that fit the needs of humanitarian organisations this is machine learning as biopower it’s predictive power will hold out the promise of saving lives producing a shift to preemption but this is effect without cause the foreclosure of futures on the basis of correlation rather than causation it constructs risk in the same way that twitter determines trending topics the result will be algorithmic states of exception according to agamben, the signature of a state of exception is ‘force-of’ actions that have the force of law even when not of the law logistic regression and neural networks generate mathematical boundaries but cybernetic exclusions will have effective force by allocating and witholding resources a process that can’t be humanised by having a humanitarian-in-the-loop because it is already a technics, a co-constituting of the human and the technical decolonial the capture, model and preempt cycle of machine learning will amplify the colonial aspects of humanitarianism unless we can develop a decolonial approach to its assertions of objectivity, neutrality and universality we can look to standpoint theory, a feminist and post-colonial approach to science which suggests that positions of social and political disadvantage can become sites of analytical advantage this is where our thinking about machine learning & ai should start from but i don’t mean by soliciting feedback from humanitarian beneficiaries participation and feedback is already a form of socialising subjects and with algorithmic humanitarianism every client interaction will be subsumed into training data they used to say ‘if the product is free, you are the product’ but now, if the product is free, you are the training data training for humanitarian ai and for the wider cybernetic governance of resilient populations machine learning can break out of this spiral through situated knowledge as proposed by donna haraway as a counterweight to the scientific ‘view from nowhere’, a situated approach that is not optional in its commitment to a particular context how does machine learning look from the standpoint of Haiti’s post-earthquake rubble or from an IDP camp no refugee in a freezing factory near the serbian border with croatia is going to be signing up for andrew ng’s mooc on machine learning any time soon how can democratic technics be grounded in the humanitarian context? people’s councils it may seem obvious that if machine learning can optimise ocado deliveries then it can help with humanitarian aid but the politics of machine learning are processes operating at the level of the pre-social one way to counter this is through popular assemblies and people’s councils bottom-up, confederated structures that implement direct democracy replacing the absence of a subject in the algorithms with face-to-face presence contesting the opacity of parallel computation with open argument and the environmentality of algorithms with direct action the role of people’s councils is not to debate for its own sake but the creation of alternative structures, in the spirit of gustav landauer’s structural renewal an emancipatory technics is one that co-constitutes active agents and their infrastructures as Landauer said, people must ‘grow into a framework, a sense of belonging, a body with countless organs and sections’ as evidenced in calais, where people collectively organised wharehouse space, van deliveries and cauldrons to cook for 100s, while regularly tasting tear gas i suggest that solidarity is an ontological category prior to subject formation collective activity is the line of flight from a technological capture that extends market relations to our intentions it is a politics of becoming - a means without end to counter ai’s effect without cause close in conclusion as things stand, machine learning and so-called ai will not be any kind of salvation for humanitarianism but will deepen the neocolonial and neoliberal dynamics of humanitarian institutions but no apparatus is a closed teleological system; the impact of machine learning is contingent and can be changed it’s not a question people versus machines but of a humanitarian technics of mutual aid in my opinion this requires a rupture with current instantiations of machine learning a break with the established order of things of the kind that badiou refers to as an event the unpredictable point of excess that makes a new truth discernible and constitutes the subjects that can pursue that new truth procedure the prerequisites will be to have a standpoint, to be situated, and to be committed it will be as different to the operations of google as the balkan aid convoys of the 1990s were to the work of the icrc on the other hand, if an alternative technics is not mobilised, the next generation of humanitarian scandals will be driven by ai

What is the relationship between citizen science and change? Engaging communities in the practice of science, especially through crowdsourcing at scale, is seen as a step towards tackling problems like global warming. The narrative is that collective measurement leads to collective awareness, which leads to collective action. However, the scientistic culture encouraged in most citizen science undermines the political determination needed to make changes in the world. It would be more productive to see citizen science as also a practice of political consciousness. ...

[This reflection on the first year of Science for Change Kosovo was commissioned by Datashift and released under a CC-BY-SA license.] Science for Change Kosovo (SfCK) is a radical citizen science project. I will try to explain what our kind of citizen science is and why it’s important, and why we decided to do citizen science in Kosovo. I will also talk about our preliminary results, our plans for the next year or so, and what I think the wider implications are for data, communities and democracy. ...

A talk for the panel on ‘Resilience and the Future of Democracy in the Smart City’ at the 25th anniversary conference of the Centre for the Study of Democracy, University of Westminster, 7th Nov 2015 I want to start by looking at what resilience and the smart city have in common. The idea of resilience comes from Holling’s original 1973 paper on ecological systems. He was looking at the balance of predator and prey, and replaced the simple idea of dynamic equilibrium with abstract concepts draw from systems theory & cybernetics. Complex systems have multiple equilibriums, and movement between these is not a collapse of the system but rather an adaptive cycle. So the population of antelope drooping by 80% is not necessarily a catastrophe, but an adaptive shift. The system persists, although in a changed form. ...

I propose we look at data science through the historical lens of phrenology. This is not to denigrate data science but to take it seriously in it’s claim to be a science, and examine its parallels with the methodological and social trajectories of phrenology as a scientific discourse. My aim is not to dismiss data science as pseudo-science but to explore the interplay of empirical and social factors in both phrenology and data science, as ways of making meaning about the world. By staying close to the practical techniques at the same time as reading them within their historical contexts, I attempt some grounded speculations about the political choices facing data science & machine learning. ...

Presented at the London Conference on Critical Thought in June 2015 graffiti art of Hannah Arendt by Patrik Wolters (alias BeneR1) in collaboration with Kevin Lasner (alias koarts) I want to warn of the possibility that algorithmic prediction will lead to the production of thoughtlessness, as characterised by Hannah Arendt. This will come from the key characteristics of the algorithmic prediction produced by data science, such as the nature of machine learning and the role of correlation as opposed to causation. An important feature for this paper is that applying machine learning algorithms to big data can produce results that are opaque and not reversible to human reason. Nevertheless their predictions are being applied in ever-wider spheres of society leading inexorably to a rise in preemptive actions. ...

I propose Data Luddism as a radical response to the productive power of big data and predictive algorithms. My starting point is not the Romantic neo-Luddism of Kirkpatrick Sale but the historical Luddism of 1811-1816, and the Luddites’ own rhetoric regarding their resistance to ‘obnoxious machines’ [1]. The Luddites’ opposition to steam-powered machines of production was based on the new social relations of power they produced, which parallels the present emergence of data-powered algorithmic machines. As discussed in my paper on Algorithmic States of Exception [2] the operations of machine learning and datamining and the production of predictive knowledge is leading to the irruption of preemption across the social field, from employment to social services and policing. The consequent loss of agency and establishment of new powers unbalanced by effective rights can be fruitfully compared to the effect of new machinery on nineteenth century woolen and silk industries. Based on this I examine key aspects of Luddite resistance for their contemporary relevance. ...

An opening provocation to the ‘DIY Science: the challenges of quality’ at the European Commission Joint Research Centre, Ispra 16-5-15 DIY science doesn’t happen inside walls with armed guards [note: this refers to the workshop venue] the question of quality is really a question of objectivity leaving the scientific hegemony doesn’t mean pure relativism; that is scare tactics use donna haraway’s situated knowledge: objectivity is about particular embodiment, not the god trick "only partial perspective promises objective vision" (haraway) the question of DIY science is a question of self-governance: learn from other struggles e.g. luddites ‘open’ alone won’t save science; learn from open data that does bad as well as good cultivate disrespect for scientific authority (not dismissal, based on historical contingency of knowledge) book proposal: "the joy of empirical discovery" modelled on alex comfort’s 1972 book "the joy of sex" science is weak, so it’s a good time to be pushing DIY science is based on social justice - DIY science should always ‘punch up’ beware recuperation: we don’t do deconstruction to benefit the religious rightwing #1980s the hack-fab complex could be an opening for neoliberalism c.f. squatting. beware assimilation! DIY science & repression: when you start to make a difference there will be arrests. what are you willing to risk? DIY science should seek social movements DIY science is transformative: coming to know should change the knower. find affinity with indigenous communities the universe is a trickster. "Feminist objectivity makes room for ironies at the heart knowledge production"

This is my statement concerning the events of July 2001, where I was one of 93 people arrested, beaten and tortured following the demonstrations against the G8. Statement of Daniel McQuillan I declare that this is a true and honest statement which I have written on Tuesday 31st July 2001. On 17th July 2001 I travelled with my friend Norman Blair on Ryanair flight FR972 from Stansted to Genoa. I went to Genoa to join protests against the exclusion of ordinary people from the decisions of the G8, which I believe are based on profit and exploitation rather than co-operation and human need. ...