Community bibliography (v1, Spring 2020)
Readings on the Histories of Artificial Intelligence
The following list of recommended readings was populated by members of the Histories of Artificial Intelligence Sawyer Seminar community. Order is alphabetical.
Kawsar Ali
I wrote an article discussing some of my research findings in light of the recent one year anniversary of the Christchurch Massacre in New Zealand that I would appreciate engagement with for this summer school. One Year On, A Search For Meaning From The Christchurch Massacre, at NewMatilda.com by Kawsar Ali on March 15, 2020.
McKane Andrus
Khovanskaya, Vera, Maria Bezaitis, and Phoebe Sengers. "The case of the strangerationist: Re-interpreting critical technical practice." Proceedings of the 2016 ACM Conference on Designing Interactive Systems. 2016.
Yung (Nicole) Au
Aouragh, M., & Chakravartty, P. (2016). Infrastructures of empire: towards a critical geopolitics of media and information studies. Media, Culture & Society, 38(4), 559–575.
Michelle Ausman
"The Allegheny Algorithm" in Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks (2018). St. Martin's Press.
Dylan Baker
I know it's not very recent, but most recently, I've been floored by Seeing like a State: How Certain Schemes to Improve the Human Condition Have Failed by James C. Scott. I'd love to go over even just the first section with a reading group!
Oshri Bar-Gil
Latour, Bruno. "On Technical Mediation – Philosophy, Sociology, Genealogy." Common Knowledge 3, no. 2 (1994): 29–64.
Eamonn Bell
Lewis, George E. "Too Many Notes: Computers, Complexity and Culture in Voyager." Leonardo Music Journal 10, no. 1 (December 2000): 33–39.
Seaver, Nick. "Captivating Algorithms: Recommender Systems as Traps." Journal of Material Culture 24, no. 4 (December 2019): 1–16.
Rachel Bergmann
Murphy, M. (2017). The Economization of Life. Duke University Press.
Sankalp Bhatnagar
Computation and Human Experience (1997) by Phil E. Agre
Alan Blackwell
Ethnographic Artificial Intelligence, a paper presented at Science in the Forest and Science in the Past, and Inventing Artificial Intelligence in Ethiopia, with Ethiopian colleagues Addisu Damena and Tesfa Tegene. Other recent publications include: a paper presented at Science in the Forest and Science in the Past, Blackwell, A.F. (2019). Objective Functions: (In)humanity and Inequity in Artificial Intelligence. HAU: Journal of Ethnographic Theory 9(1), 137-146; Blackwell, A.F. (2019). Artificial intelligence and the abstraction of cognitive labour. In M. Davis (Ed.), Marx200: The Significance of Marxism in the 21st Century. London: Praxis Press, pp. 59–68.
Georgina Born
Adrian Mackenzie's Machine Learners: Archaeology of a Data Practice (MIT Press, 2017)
Xavier Serra, 'The computational study of a musical culture through its digital traces', Acta Musicologica, v. 89, n. 1 (2017) pp. 24–44.
Audrey Borowski
Descartes, Discourse on the Method and Treatise on Man; Leibniz, 'Explanation of Binary Arithmetic'; Jonathan Swift, Gulliver's Travels; La Mettrie, Man Machine; ETA Hoffmann's, The Sandman; Thomas Bayes, An Essay towards solving a Problem in the Doctrine of Chances; etc.
Dan Bouk
Joanna Radin, "Digital Natives," Osiris 32 (2017): 43–64.
Nicholas Bradley
Pierre Teilhard de Chardin's "The Phenomenon of Man".
Rachel Brydolf-Horwitz
Surrogate Humanity (Neda Atanasoski and Kalindi Vora)
Amazon Echo and the Aesthetics of Whiteness (Thao Phan)
The Defecating Duck, or, the Ambiguous Origins of Artificial Life (Jessica Riskin)
Eugenia Canas
Barney, D., "The Culture of Technology in Canada". Canadian Issues/Thèmes Canadiens. Winter 2007. 28–31
Emily Capstick
Kanta, D., Dillon, S., Cave, S., AI Narratives: A History of Imaginative Thinking about Intelligent Machines (Oxford, 2020), pp. 302–346
Michael Castelle
Maniglier, P. (2011). "Processing Culture: 'Structuralism' in the History of Artificial Intelligence". In S. Franchi & F. Bianchini (Eds.), The Search for a Theory of Cognition: Early Mechanisms and New Ideas (pp. 149–177). Rodopi.
Gary Charles
Machines that Morph Logic: Neural Networks and the Distorted Automation of Intelligence as Statistical Inference by Matteo Pasquinelli
Yaqub Chaudhary
Constructions of the Mind: Artificial Intelligence and the Humanities, A special issue edited by Stefano Franchi and Güven Güzeldere
Tom Coggins
1. Agency Laundering and Information Communication Technologies, by Alan Rubel, Clinton Castro and Adam Pham
In this paper the authors make the case that actors 'launder their agency' when they rely on regulatory information technologies to make their decisions for them. For instance, when judges in the US appeals to advice from technologies such as COMPAS to decide a convict's prison time, they may fail to acknowledge their own agency in this decision and instead 'launder' it to a machine. I find this article particularly interesting, as within the ethical literature on AI researchers tend to focus on why technologies of this kind shouldn't be allowed to make decisions that may harm humans, whereas the authors highlight that the technology itself isn't the main concern here, but rather the fact that actors in positions of power are willing to distance themselves from their own decisions. This is especially troubling in legal contexts, as judges are traditionally viewed as the source of legitimacy during conviction hearings – and are therefore responsible for their decisions – but effectively grant machines the power to decide whether someone will spend the rest of their lives incarcerated.
2. Hyper-Nudge by Karen Yeung
In this paper legal scholar Karen Yeung draws a distinction between regular nudges (as made famous by Thaler and Sunstein) and hyper-nudges. Regular nudges are static, one-time prompts that are often mediated via technology, which persuade individuals to alter their behaviour in accordance to another more powerful actor's designs. Hyper-nudges, on the other hand, occur when an actor uses big data techniques to access another party or parties information, then uses these insights to create nudges that they predict will effectively prompt their targets to perform specific actions. Due to their access to big data techniques, the more powerful actors can develop more effective ways to nudge their target over time. After introducing this idea, Yeung argues that hyper-nudging should be treated as illegitimate within liberal democracies, as it is deceptive, lacks transparency and undermines its targets autonomy. I find this paper particularly relevant for discussions on AI marketed towards consumers, as these technologies often express automated hyper-nudging capabilities, for instance when they track and trace their users, then adapt their interfaces or outputs around the input data provided by their users. Examples here include smart toys like Hello Barbie or My Friend Cayla, which employ voice-recognition software to prompt children to share private information with them, then reply with responses designed to keep their users engaged.
Fernando Delgado
If relevant, I would be happy to guide the group in a close reading discussion on a relatively recent "fairness in ML" research article. Rather than choosing a computational oriented piece, I would choose an interpretive/critical-oriented piece from this interdisciplinary research community. In addition to reviewing the piece in the context of contemporary fairness in AI/ML literature, I would want to engage with the longer arc of computational and sociological thinking it engages with. The following are candidate articles I propose for this:
Selbst et al. 2019. Fairness and Abstraction in Sociotechnical Systems
Selbst and Barocas. 2018. The Intuitive Appeal of Explainable Machines
Barocas and Selbst. 2016. Big Data's Disparate Impact.
Ranjodh Singh Dhaliwal
John von Neumann's The Computer and the Brain or Perceptrons by Marvin Minsky and Seymour Papert or my paper (abstract above)!
Max Dovey
Green B, Viljoen S. Algorithmic Realism: Expanding the Boundaries of Algorithmic Thought. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAT*). 2020.
Stefanie Felsberger
Virgina Eubanks. 2018. Automating Inequality
Arun Appadurai. 1993. "Number in the Colonial Imagination." In Orientalism and the Postcolonial Predicament: Perspectives on South Asia
Timothy Mitchell. 2002. "The Character of Calculability." In Rule of Experts: Egypt, Techno-Politics, Modernity.
Fabian Ferrari
Tsing, A. (2009). Supply chains and the human condition. Rethinking Marxism, 21(2), 148–176.
Johan Gärdebo
I recommend a read of Hannes Alfvén's The Tale of the Big Computer (1966), published under the pseudonym Olof Johanneson (translated into English by Coward-McCann, Inc. 1968). Alfvén's The Tale is a sci-fi critique of Swedish Social Democracy's policies for social engineering, computerising society, and automating labour. It has since then been influential for subsequent discourse on AI, for example Abbe Mohwsowitz's review (1997) and George Dyson's book Turing's Cathedral (Pantheon Books, 2012).
Colin Garvey
Garvey, Colin. "Artificial Intelligence and Japan's Fifth Generation: The Information Society, Neoliberalism, and Alternative Modernities" Pacific Historical Review, Vol. 88 No. 4, Fall 2019; (pp. 619–658).
Taube, Mortimer. Computers and Common Sense: The Myth of Thinking Machines. New York, NY: Columbia University Press, 1961.
Patrick Graham
Ian Hacking, "Biopower and the Avalanche of Printed Numbers," Humanities in Society (1982).
Daniel Greene
Hayek (1945) "The Use of Knowledge in Society"
Sarah Hamid
Op-Ed authored 10/2019 by the Stop LAPD Spying Coalition: Location-Based Policing: New LAPD Technologies, Same Racisms. This is a community-authored document, meaning it serves as an example of the kind of research possible through community archiving.
Chris Hesselbein
Lev Manovich's recent book AI Aesthetics. I think the aesthetic implications of algorithmic practices is a fascinating and relatively neglected of academic research, and I'm especially keen to explore the relationship between digital technologies and embodied experiences (how we see, feel, and value our bodies as they are read and (re)configured algorithmically).
Brian Justie
I would perhaps suggest a pairing of two articles that I find to be extraordinarily compelling. Both authors, albeit in slightly different ways, attempt to bring the seemingly divergent genealogies of information theory and aesthetic theory into a productive tension, a task which holds considerably import, I think, for AI historians.
Campolo, Alexander. "Signs and Sight: Jacques Bertin and the Visual Language of Structuralism." Grey Room 78, (2020): 34–65.
Geoghegan, Bernard Dionysius. "From Information Theory to French Theory: Jakobson, Lévi-Strauss, and the Cybernetic Apparatus." Critical Inquiry 38, no. 1 (2011): 96–126.
Sam Kellogg
I have two reading recommendations, both of which don't deal directly with AI per se but which I think could illuminate the broader conversation, perhaps in generative ways. If you are convinced by my argument that error detection and correction are central to artificial intelligence, then reading Richard Hamming's foundational "Error Detecting and Error Correcting Codes" (1950) alongside such foundational work as McCulloch and Pitts (1943) and Turing (1950) could be very fruitful.
My second recommendation is for a foundational anti-colonial Cuban essay by Roberto Fernández Retamar entitled "Caliban: Notes towards a Discussion of Culture in Our America" (1974), which productively deals with the question of dominant and marginalized languages, logics, and epistemologies in the context of the global Cold War. It could nicely counterpoise a text like Audre Lorde's "The Master's Tools Will Never Dismantle the Master's House" in a discussion about reappropriating oppressive technologies and logics towards new ends (1984).
Sanaa Khan
Surrogate Humanity: Race, Robots, and the Politics of Technological Futures – Atanasoski and Vora
Labor in the Digital Economy – Ursula Huws
On Hell – Johanna Hedva
Buhle Khanyile and Rachel Adams
Tegan Bristow (2017) Post African futures: positioning the globalized digital within contemporary African cultural and decolonizing practices, Critical African Studies, 9:3, 281–301.
Levin Kim
Hoffmann, A.L. (2019) Where fairness fails: Data, algorithms, and the limits of antidiscrimination discourse. Information, Communication, & Society, 22(7), 900–915.
Jaime Kirtz
Roberts, Sarah T. 2019. Behind the Screen: Content Moderation in the Shadows of Social Media. New Haven: Yale University Press.
Snigdha Kumar
Dyer-Witheford, N., Kjøsen, A. M., & Steinhoff, J. (2019). Inhuman Power. Artificial Intelligence and the Future of Capitalism. Pluto Press
Atanasoski, N., & Vora, K. (2019). Surrogate Humanity: Race, Robots, and the Politics of Technological Futures. Duke University Press.
Rachel Kuo
Sylvia Wynter (2003) Unsettling the coloniality of being/power/truth/freedom: Towards the human, after man, its overrepresentation – an argument. CR: The New Centennial Review 3, 3, 257–337. I recently used this text in the piece with Anne Washington, "Whose Side Are Ethics Codes On Anyway?" Within fields of computing and studies of algorithmic sorting, Wynter's discussion of how a "master code" organizes society into a hierarchal order where categories of protected life imply a category of unprotected life can be helpful towards thinking about how hierarchies continue to be embedded in technological systems (those "wired" versus those "left behind", "users" versus "nonusers", etc).
Crystal Lee
While histories of AI thus far have dealt extensively with issues of race (Benjamin 2019, Noble 2018), gender (Hicks 2017), and class (Gray and Suri 2019, Eubanks 2019), there has been relatively little focus on work that deals more specifically with the intersection between AI and disability. Sustained engagement with crip theory would improve, for example, research on the history of autonomous vehicles, as it could connect the history of anthropometry – which is often used to determine whether or not the car is approaching a human at a crosswalk – with a longer history of eugenics and other scientific determinations of what it means to be human. As such, I would recommend this book on the longer history of technological design and disability: Aimi Hamraie, Building Access: Universal Design and the Politics of Disability (Minneapolis, MN: University of Minnesota Press, 2017).
Jason Lewis
Lewis, Jason Edward, Noe Arista, Archer Pechawis and Suzanne Kite. "Making Kin with the Machines." Journal of Design and Science, vol. Summer 2018, no. 3.5, July 2018.
Xiaochang Li
"Pandemonium: A Paradigm for Learning" by O.G. Selfridge
Cindy Lin
A shameless plug: I will love to recommend Technoprecarious by Precarity Lab (Goldsmiths Press/The MIT Press, October 2020). It is a co-authored book I have written with 11 other media scholars, critical theorists, science studies scholars and information scholars on the implications of digital economies and platforms on vulnerable peoples across regions. The other co-authors are Anna Watkins Fisher, Silvia Lindtner, Ivan Chaar-Lopez, Cengiz Salman, McKenzie Wark, Kalindi Vora, Jackie Wang, Cass Adair, Lisa Nakamura, and Meryem Kamil. Chapters will cover the implications of algorithmic bias in case studies such as medicaid systems in the U.S. Midwest to technologies of surveillance in the US-Mexico border.
Iris Long
I could recommend: Alleys of Your Mind: Augmented Intelligence and Its Traumas, edited by Matteo Pasquinelli
Nikola Mlađenović
Carl Schmitt, The Buribunks
Priya Murali Krishna-Sharada
The Optimist's Telescope – Thinking Ahead In A Reckless Age (by Bina Venkataraman) – With examples from ancient Pompeii to modern day Fukushima, she dispels the myth that human nature is impossibly reckless and highlights the surprising practices each of us can adopt in our own lives, and the ones we must fight for as a society. Instant gratification is the norm today, in our lives, our economy, and our politics. Many of us have forgotten how to make smart decisions for the long run. This book highlights the importance of thinking strategically about the future (rather than having a highly myopic vision,) and in the context of this Summer School, I believe that the entire book (or selected chapters) would provide the reading community the underpinnings of the fact that – most of us see the future only after it becomes the past. While the Summer School addresses the histories of AI, I personally feel that it is crucial to short-circuit our shortsightedness and look across the past-present-future vantage points of AI technologies. The author teaches in the program on Science, Technology, and Society at MIT, and is a former journalist for The New York Times. An alumna of Brown University and the Harvard Kennedy School, she is now the Director of the Global Policy Initiatives at the Broad Institute of MIT and Harvard.
Eva Myrczik
D'Ignazio, Catherine, and Klein, Lauren. Data Feminism. The MIT Press, 2020.
Jeff Nagy
Mara Mills and Jonathan Sterne. "Afterword II: Dismediation—Three Proposals, Six Tactics," in Disability Media Studies (ed. Elizabeth Ellcessor and Bill Kirkpatrick). This short paper isn't specifically focused on AI, but it's an incredibly rich and provocative call to re-think how critical scholars approach media and disability. Mills and Sterne's arguments in these few pages have served as the jumping off point for much of my own work.
Abigail Nieves Delgado
Desrosières, A. (1988) Introduction, in The Politics of Large Numbers. Cambridge: Harvard University Press.
Michael Ortiz
Browne, Simone. Dark Matters: On the Surveillance of Blackness. Durham: Duke University Press, 2015.
Sharad Pandian
Jon Agar, The Government Machine
Matteo Pasquinelli
I will be happy to share chapters of my forthcoming book. One chapter has been already published by Radical Philosophy. Also a short article for the (non academic) art journal e-flux journal gives a good overview of my research. Matteo Pasquinelli, "On the Origins of Marx's General Intellect", Radical Philosophy, 2.06, winter 2019, 43–56; Matteo Pasquinelli, "3000 Years of Algorithmic Rituals: The Emergence of AI from the Computation of Space," e-flux journal #101, June 2019. Donald Knuth, "Ancient Babylonian Algorithms".
Gabriel Pereira
"Toward a Critical Technical Practice: Lessons Learned in Trying to Reform AI" by Philip E. Agre.
Kuba Piwowar
I found this Wendy Hui Kyong Chun lecture especially inspiring in my work. She expanded her lecture in an article titled "Queerying Homophily", a part of an open-source book called Pattern Discrimination.
Aaron Plasek
Louis Fein. "Impotence Principles for Machine Intelligence." Proceedings of the IEEE Workshop on Pattern Recognition, ed. Laveen Kanal. 1968.
Ian Hacking. The Social Construction of What? Harvard UP: Cambridge & London. 1999.
Alex Hanna et. al. Towards a Critical Race Methodology in Algorithmic Fairness. Conference on Fairness, Accountability, and Transparency (FAT*), Barcelona, Spain. January 27–30, 2020.
Anne Ploin
Schaffer's article "OK Computer" – on the historical co-production of human and machine capability – I think is a must-read. It illuminated for me the ways in which human capability must essentially be devalued in order for overhyped accounts of machine potential to circulate effectively; read complementarily with feminist scholarship on labour, the piece makes the particulars of this devaluation become clear.
Marie-Therese Png
Ricaurte P (2019) Data epistemologies, the coloniality of power, and resistance. Television & New Media 20(4):350–365
Green B (2019) "Good" isn't good enough. In: Proceedings of the AI for Social Good workshop at NeurIPS
Costanza-Chock S (2018) Design Justice, AI, and Escape from the Matrix of Domination: Journal of Design and Science
#unsettle initiative at Data & Society
Anna Ridler
"12 Hours of ImageNet" as part of Nicolas Malevé's project "Exhibiting ImageNet", July – Sept 2019
"Image Exhaust", Photographers Gallery, ongoing
"ImageNet Roulette", Trevor Paglen, Sept 2019 (Archived)
"Excavating AI: The Politics of Images in Machine Learning Training Sets", Kate Crawford & Trevor Paglen, Sept 2019
"Myriad (Tulips)", Anna Ridler, 2018
"Mosaic Virus", Anna Ridler, 2019 (these two works are contextualized by Elaine Ayers' writing "Using AI to Produce 'Impossible' Tulips" in Hyperallegeric)
Johannah Rodgers
Rodgers, J. The Genealogy of an Image, or, What Does Literature (Not) Have To Do with the History of Computing?: Tracing the Sources and Reception of Gulliver's "Knowledge Engine". Humanities 2017, 6, 85. The illustration of the "knowledge engine" included in early editions of Gulliver's Travels is an engraving of a sketch from the notebook of Lemuel Gulliver. In other words, it is a purely fictional object. Yet, Swift's fictional invention and its graphic representations have become part of the documented historical lineage of computing machines. Furthermore, one of Swift's purposes for inventing the "knowledge engine" was to satirize the scientific and technical cultures that now claim it as part of their history. As one piece of the elaborate discursive and material code of Gulliver's Travels, "the knowledge engine," its sources, and its reception offer some unique insights into the relationships that exist amongst factual and fictional narratives, scientific and humanistic discourse, words and images, and print and digital technologies. Although numerous scientific and philosophical texts have been cited as possible sources informing Swift's satirical invention, this article considers a lesser known one, John Peter's 1677 pamphlet Artificial Versifying, or the Schoolboy's Recreation, which is itself a print-based textual machine for generating lines of Latin hexameter verse.
Sananda Sahoo
Habermas' Structural Transformations
Konstantinos Sakalis
On the long-run history of the rhetoric of presenting all kinds of computing artifacts as 'intelligent', and the black-boxing/encasement of the analog-feminine-labor so as to display the digital-masculine-machine as 'intelligent', we could read extracts from Aristotle Tympas, Calculation and Computation in the Pre-electronic Era (Springer 2017), Section 2.4 (pages 18–19), entitled "An Ingenious Instrument", Section 6.4 (pages 198–201), entitled "Can be put in the hands of a girl", and Section 6.4 (pages 201–207) "The True automatic machine belongs to a possible rather than an actual class" .
Pietro Santachiara
Collins, Harry. Artifictional Intelligence
Maikki Sipinen
History's message about Regulating AI by Tom Wheeler
Claire Y. Song
Deep Learning (2019) by John D. Kelleher is a good introductory book to the technical terms of deep learning, at the core of most up-to-date AI technology, providing those of us interested in interpreting AI's current pervasiveness with a concise history of its development and a toolkit for checking the technical language.
Dave Stokes
Algorithms of Oppression by Safiya Umoja Noble
Apolline Taillandier
Binns, Reuben. "Fairness in Machine Learning: Lessons from Political Philosophy." ArXiv:1712.03586 [Cs], December 10, 2017.
Malabou, Catherine. Morphing Intelligence: From IQ Measurement to Artificial Brains. Trans. Carolyn Shread. New York: Columbia University Press, 2019. 198 pp.
Simon Taylor
Yampolskiy, Roman V., and Venu Govindaraju. "Behavioural Biometrics: A Survey and Classification." International Journal of Biometrics 1, no. 1 (2008): 81–113.
Quinlan, J. R. "Induction of Decision Trees." Machine Learning 1, no. 1 (March 01 1986): 81–106.
Nishita Trisal
Deleuze, Postscript on the Societies of Control
Rebecca Uliasz
Matteo Pasquinelli, "How a Machine Learns and Fails: A Grammar of Error for Artificial Intelligence". Spheres, n. 5, 2019.
Christian Vater
Friedrich A. Kittler (2014): The Truth of the Technological World: Essays on the Genealogy of Presence. Ed. and Comm. Ulrich Gumbrecht, Stanford: SUP. [Selection of Writings by Kittler]
Charles Babbage (1832): On the Economy of Machinery and Manufactures, London: Knight
Gideon Freudenthal, Peter McLaughlin (2009): The Social and Economic Roots of the Scientific Revolution. Texts by Boris Hessen and Henryk Grossmann, Berlin: Springer. (Boston Studies in the Philosophy of Science 278).
Maaike Verbruggen
Armstrong, Stuart, Kaj Sotala, and Seán S.Ó Héigeartaigh. "The Errors, Insights and Lessons of Famous AI Predictions – and What They Mean for the Future." Journal of Experimental and Theoretical Artificial Intelligence 26, no. 3 (2014): 317–42.
Aleksandra Vulic
The chapter 'The Enlightenment Automaton in the Modern Industrial Age' out of Adelheid Voskuhl's Androids in the Enlightenment critiques the common assumption about the conditions of the emergence of automata in Enlightenment culture, observing instead that it was the technologies that were seemingly unique, individual and artisan-produced and the human individuals that were mass-produced in uniform, identical ways and in large numbers as part of sentimentality culture.
Rosa Wevers
Hegde, Radha S. 2019. "Itinerant Data: Unveiling Gendered Scrutiny at the Border." Television and New Media 20 (6): 617–33.
Tanja Wiehn
D'Ignazio, Catherine, and Klein, Lauren. Data Feminism. The MIT Press, 2020.
Maranke Wieringa
Kroll, Joshua. (2018) The fallacy of inscrutability. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
Lauren Wilcox
Atanasocki and Vora's Surrogate Humanity (Duke 2019)
Hayles, How We Became Posthuman (Chicago 1999)
Melody Xu
Ossmy, O.*, Hoch, J. E.*, MacAlpine, P., Hasan, S., Stone, P., & Adolph, K. E. (2018). Variety wins: Soccer-playing robots and infant walking. Frontiers in Neurorobotics, 12: 19. [Ossmy & Hoch, shared first authorship].
Mark Thomas Young
Noble, David. Forces of Production: A Social History of Industrial Automation (New Jersey: Transaction Publishers, 2011)
Ekbia, Hamid R. & Bardi, Bonnie. Heteromation, and Other Stories of Computing and Capitalism (Massachusetts: MIT Press, 2017)
Wajcman, Judy "Automation: Is it Really Different this Time?" British Journal of Sociology 68(1) 2017 pp. 119–127
Brett Zehner
Zehner, Brett. Machines of Subjection: Notes on a Tactical Approach to Artificial Intelligence.
Zi-yong Zhang
Some of Marx's works, such as the economic manuscript of 1857–1858 (i.e. the outline of political economy criticism), or some chapters of CAPITAL. This will help us to understand the history and development of artificial intelligence.