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Resisting AI

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Resisting AI: An Anti-fascist Approach to Artificial Intelligence is a book on artificial intelligence (AI) by Dan McQuillan, published in 2022 by Bristol University Press.

Synopsis

Mc Quillan's Resisting AI[1] contrasts optimistic visions about AI's potential by arguing that AI may best be seen as a continuation and reinforcement of bureaucratic forms of discrimination, violence, ultimately fostering authoritarian outcomes.[2] For the author AI's promises of objective calculability is antithetical to an egalitarian and just society.[3][4]

Main

The author uses the expression ‘AI violence’ to describe how – based on opaque proprietary algorithms – various actors can inflict damage or discriminate categories of people from accessing jobs, loans, medical care or other benefits.

The analysis goes beyond the known critique of AI systems fostering precarious labour markets, addressing necropolitics, the politics of who is entitled to live, and who to die.[2]

Although the author offers a brief history of machine learning at the beginning of the book – with its need for 'hidden and undercompensated labour',[5] he is concerned more with the social impacts of AI rather than with its technical aspects.[6][5] McQuillan sees AI as the continuation of existing bureaucratic systems that already marginalize vulnerable groups - aggravated by the fact that AI systems trained on existing data are likely to reinforce existing discriminations, e.g. in attempting to optimize welfare distribution based on existing data patterns.[6] In elaborating on the continuation between existing bureaucratic violence and AI, he reconnect to Hannah Arendt concept of thoughtlessness. The thoughtless bureaucrat of Arendt's Eichmann in Jerusalem: A Report on the Banality of Evil[7] becomes now the algorithm that, lacking intent, cannot be accountable, and is thus endowed with an 'algorithmic thoughtlessness', pp. 62-63.[1]

AI can support the diffusion of states of exception:

AI has an inbuilt tendency toward creating partial states of exception. AI is not only a technology that is impossible to properly regulate but a mechanism for multiplying exceptions more widely, p. 75.[1]

An example of a scenario where AI systems of surveillance could bring discrimination to a new high is the initiative to create LGBT-free zones in Poland, pp. 76–77.)[1].[6]

Skeptical of ethical regulations to control the technology, Mc Quillan suggests people's councils and workers’ councils, and other forms of citizens agency to resist AI.[6] A chapter entitled 'Post-Machine Learning' makes an appeal for resistance via currents of thought from Feminist science (standpoint theory), Post-normal science (Extended Peer Communities), and New materialism. Among the virtuous example of resistance - to be possibly adopted by the AI workers themselves - Mc Quillan notes (p. 126,141[1]) the Lucas Plan of the workers of Lucas Aerospace Corporation,[8] where a workforce declared redundant took control reorienting the enterprise toward useful products.[9] In an intetview about the book, McQuillan defines himself as an 'AI abolitionist'.[10]

Notwithstanding the ‘fascist’ in the title of the work, the author notes[9] that while not all AI is fascist, this emerging technology of control may end up being deployed by fascist or authoritarian regimes. On the critical side, more than one review[3][6] points to a partial disconnect between the broad social critique of the work and its anchoring to the workings of AI.

See also

References

  1. ^ a b c d e McQuillan, D. (2022). Resisting AI: An Anti-fascist Approach to Artificial Intelligence, Bristol University Press.
  2. ^ a b Rossi, N. (2022, July 12). Resisting AI - A Review. Retrieved from https://orwellsociety.com/resisting-ai-a-review/.
  3. ^ a b Selkälä, T. (2022). Healthily futile: a quest for a different AI. Justice, Power and Resistance, 5(3), 322–330.
  4. ^ McKenna, B. (2023). Resisting artificial intelligence as we know it. Computer Weekly, 14–14.
  5. ^ a b Golumbia, David (October 1, 2023). "Resisting AI: An Anti-fascist Approach to Artificial Intelligence, by Dan McQuillan". Critical AI. 1 (1–2). doi:10.1215/2834703x-10734967. S2CID 263647209.
  6. ^ a b c d e Stürmer, M., & Carrigan, M. (2023, November 16). Resisting AI: An Anti-fascist Approach to Artificial Intelligence – review. Retrieved from https://blogs.lse.ac.uk/impactofsocialsciences/2023/11/16/resisting-ai-an-anti-fascist-approach-to-artificial-intelligence-review/
  7. ^ Arendt, H. (1963). Eichmann in Jerusalem: A Report on the Banality of Evil de Hannah Arendt, Faber & Faber.
  8. ^ "The Lucas Plan: How Greens and trade unionists can unite in common cause". Theecologist.org. 2 November 2016.
  9. ^ a b Klovig Skelton, S. (2023). AI interview: Dan McQuillan, critical computing expert. Computer Weekly. Retrieved from https://www.computerweekly.com/news/366537843/AI-interview-Dan-McQuillan-critical-computing-expert.
  10. ^ McQuillan, D., & Kremakova, M. (2023). Dan McQuillan in conversation: Big data, deep learning, and hold the apocalypse. The Sociological Review Magazine. doi:10.51428/tsr.inuk8253.
  • Amoore, L. (2020). Cloud Ethics, Algorithms and the Attributes of Ourselves and Others, Duke University Press. Retrieved from Duke University Press - Cloud Ethics
  • O’Neil, C. (2016). Weapons of math destruction : how big data increases inequality and threatens democracy, Random House Publishing Group.
  • Salais, R. (2022). “La donnée n’est pas un donné”: Statistics, Quantification and Democratic Choice. In The New Politics of Numbers: Utopia, Evidence and Democracy, Andrea Mennicken and Rober Salais, Palgrave Macmillan, pp. 379–415.
  • Supiot, A. (2017). Governance by Numbers: The Making of a Legal Model of Allegiance, Hart Publishing.
  • Teachout, Z. (2022). The Boss Will See You Now | Zephyr Teachout. New York Review of Books. Retrieved from The Boss Will See You Now | Zephyr Teachout
  • Zuboff, S. (2019). The Age of Surveillance Capitalism : the fight for a human future at the new frontier of power, PublicAffairs.
  • Kantor, J., Sundaram, A., Aufrichtig, A., & Taylor, R. (2022, August 15). The Rise of the Worker Productivity Score. The New York Times. Retrieved from https://www.nytimes.com/interactive/2022/08/14/business/worker-productivity-tracking.html


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