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| Author | Dan McQuillan |
|---|---|
| Language | English |
| Subjects | Artificial Intelligence |
| Publisher | Bristol University Press |
Publication date | 2022 |
| Pages | 190 |
| ISBN | 978-1529213508 |
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.
Resisting AI 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 and violence, ultimately fostering authoritarian outcomes. [1] For McQuillan, AI's promises of objective calculability is antithetical to an egalitarian and just society. [2] [3] McQuillan uses the expression ‘AI violence’ to describe how – based on opaque algorithms – various actors can inflict damage or discriminate categories of people from accessing jobs, loans, medical care or other benefits. [1]
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. [1] [4] Although McQuillan offers a brief history of machine learning at the beginning of the book – with its need for 'hidden and undercompensated labour', [4] he is concerned more with the social impacts of AI rather than with its technical aspects. [5] [4] 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, [5] ultimately creating a system of 'self-reinforcing social profiling'. [6]
In elaborating on the continuation between existing bureaucratic violence and AI, McQuillan connects to Hannah Arendt's concept of the thoughtless bureaucrat in Eichmann in Jerusalem: A Report on the Banality of Evil , which now becomes the algorithm that, lacking intent, cannot be accountable, and is thus endowed with an 'algorithmic thoughtlessness'. [7] : 62–63
McQuillan defends the ‘fascist’ in the title of the work by arguing that while not all AI is fascist, this emerging technology of control may end up being deployed by fascist or authoritarian regimes. [8] For McQuillan AI can support the diffusion of states of exception, as a technology impossible to properly regulate and a mechanism for multiplying exceptions more widely. 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. [7] : 75–77 [5]
Skeptical of ethical regulations to control the technology, McQuillan suggests people's councils and workers’ councils, and other forms of citizens agency to resist AI. [5] 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 - McQuillan notes (p. 126,141 [7] ) the Lucas Plan of the workers of Lucas Aerospace Corporation, [9] where a workforce declared redundant took control reorienting the enterprise toward useful products. [8] In an intetview about the book, McQuillan defines himself as an 'AI abolitionist'. [10] The book itself reads as an extended essay [11]
On the critical side, a review takes exception to the 'nightmarish visions of Big Brother' offered by McQuillan; while many element of AI may pose concern, a critique cannot be based on a caricature of what AI is, missing which the work of McQuillan is 'less of a theory and more of a Manifesto'. [2] Another review notes 'a disconnect between the technical aspects of AI and the socio-political analysis McQuillan provides'. [5]
For a reviewer, although the book was published before the ChatGPT and Large Language Model debate heated up, the book has not lost relevance to the AI discussion. [12]
Videos [12] [13] and podcasts [11] [14] [15] with an interest in AI and emerging Technology have discussed the book.
Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or other animals. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to surpass many previous approaches in performance.
The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.
The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen. The seeds of modern AI were planted by philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.
The following outline is provided as an overview of and topical guide to artificial intelligence:
Artificial intelligence is a recurrent theme in science fiction, whether utopian, emphasising the potential benefits, or dystopian, emphasising the dangers.
Machine ethics is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherwise known as artificial intelligent agents. Machine ethics differs from other ethical fields related to engineering and technology. Machine ethics should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with the grander social effects of technology.
Artificial intelligence in healthcare is a term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to copy human cognition in the analysis, presentation, and understanding of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.
Explainable AI (XAI), often overlapping with Interpretable AI, or Explainable Machine Learning (XML), either refers to an AI system over which it is possible for humans to retain intellectual oversight, or to the methods to achieve this. The main focus is usually on the reasoning behind the decisions or predictions made by the AI which are made more understandable and transparent. XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision.
Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.
ACM Conference on Fairness, Accountability, and Transparency is a peer-reviewed academic conference series about ethics and computing systems. Sponsored by the Association for Computing Machinery, this conference focuses on issues such as algorithmic transparency, fairness in machine learning, bias, and ethics from a multi-disciplinary perspective. The conference community includes computer scientists, statisticians, social scientists, scholars of law, and others.
The sociologyof quantification is the investigation of quantification as a sociological phenomenon in its own right.
Government by algorithm is an alternative form of government or social ordering where the usage of computer algorithms is applied to regulations, law enforcement, and generally any aspect of everyday life such as transportation or land registration. The term "government by algorithm" has appeared in academic literature as an alternative for "algorithmic governance" in 2013. A related term, algorithmic regulation, is defined as setting the standard, monitoring and modifying behaviour by means of computational algorithms – automation of judiciary is in its scope. In the context of blockchain, it is also known as blockchain governance.
Regulation of algorithms, or algorithmic regulation, is the creation of laws, rules and public sector policies for promotion and regulation of algorithms, particularly in artificial intelligence and machine learning. For the subset of AI algorithms, the term regulation of artificial intelligence is used. The regulatory and policy landscape for artificial intelligence (AI) is an emerging issue in jurisdictions globally, including in the European Union. Regulation of AI is considered necessary to both encourage AI and manage associated risks, but challenging. Another emerging topic is the regulation of blockchain algorithms and is mentioned along with regulation of AI algorithms. Many countries have enacted regulations of high frequency trades, which is shifting due to technological progress into the realm of AI algorithms.
The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI); it is therefore related to the broader regulation of algorithms. The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally, including in the European Union and in supra-national bodies like the IEEE, OECD and others. Since 2016, a wave of AI ethics guidelines have been published in order to maintain social control over the technology. Regulation is considered necessary to both encourage AI and manage associated risks. In addition to regulation, AI-deploying organizations need to play a central role in creating and deploying trustworthy AI in line with the principles of trustworthy AI, and take accountability to mitigate the risks. Regulation of AI through mechanisms such as review boards can also be seen as social means to approach the AI control problem.
Ethics of quantification is the study of the ethical issues associated to different forms of visible or invisible forms of quantification. These could include algorithms, metrics/indicators, statistical and mathematical modelling, as noted in a review of various aspects of sociology of quantification.
The Artificial Intelligence Act is a proposed European Union regulation on artificial intelligence in the European Union. Proposed by the European Commission on 21 April 2021 and not yet enacted, it would introduce a common regulatory and legal framework for artificial intelligence.
In machine learning, the term stochastic parrot describes large language models which generate convincing language but do not understand the meaning of the language they process. The term was coined by Emily M. Bender in the 2021 artificial intelligence research paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜" by Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell.
