END NOTES

[1] Minsky currently faces serious allegations related to convicted pedophile and rapist Jeffrey Epstein. Minsky was one of several scientists who met with Epstein and visited his island retreat where underage girls were forced to have sex with members of Epstein’s coterie. As scholar Meredith Broussard observed, this was part of a broader culture of exclusion that became endemic in AI: “as wonderfully creative as Minsky and his cohort were, they also solidified the culture of tech as a billionaire boys’ club. Math, physics, and the other “hard” sciences have never been hospitable to women and people of color; tech followed this lead.” See Meredith Broussard, Artificial Unintelligence: How Computers Misunderstand the World (Cambridge, Massachusetts, and London: MIT Press, 2018), 174.

[2] See Daniel Crevier, AI: The Tumultuous History of the Search for Artificial Intelligence (New York: Basic Books, 1993), 88.

[3] Minsky gets the credit for this idea, but clearly Papert, Sussman, and teams of “summer workers” were all part of this early effort to get computers to describe objects in the world. See Seymour A. Papert, “The Summer Vision Project,” July 1, 1966, https://dspace.mit.edu/handle/1721.1/6125. As he wrote: “The summer vision project is an attempt to use our summer workers effectively in the construction of a significant part of a visual system. The particular task was chosen partly because it can be segmented into sub-problems which allow individuals to work independently and yet participate in the construction of a system complex enough to be a real landmark in the development of ‘pattern recognition’.”

[4] Stuart J. Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd ed, Prentice Hall Series in Artificial Intelligence (Upper Saddle River, NJ: Prentice Hall, 2010), 987.

[5] In the late 1970s, Ryszard Michalski wrote an algorithm based on “symbolic variables” and logical rules. This language was very popular in the 1980s and 1990s, but, as the rules of decision-making and qualification became more complex, the language became less usable. At the same moment, the potential of using large training sets triggered a shift from this conceptual clustering to contemporary machine-learning approaches. See Ryszard Michalski, “Pattern Recognition as Rule-Guided Inductive Inference.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2, 349–361, 1980.

[6] There are hundreds of scholarly books in this category, but for a good place to start, see W. J. T. Mitchell, Picture Theory: Essays on Verbal and Visual Representation, Paperback ed., [Nachdr.] (Chicago: University of Chicago Press, 2007).

[7] M. Lyons et al., “Coding Facial Expressions with Gabor Wavelets,” in Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition (Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan: IEEE Comput. Soc, 1998), 200–205, https://doi.org/10.1109/AFGR.1998.670949.

[8] As described in the AI Now Report 2018, this classification of emotions into six categories has its root in the work of the psychologist Paul Ekman. “Studying faces, according to Ekman, produces an objective reading of authentic interior states—a direct window to the soul. Underlying his belief was the idea that emotions are fixed and universal, identical across individuals, and clearly visible in observable biological mechanisms regardless of cultural context. But Ekman’s work has been deeply criticized by psychologists, anthropologists, and other researchers who have found his theories do not hold up under sustained scrutiny. The psychologist Lisa Feldman Barrett and her colleagues have argued that an understanding of emotions in terms of these rigid categories and simplistic physiological causes is no longer tenable. Nonetheless, AI researchers have taken his work as fact, and used it as a basis for automating emotion detection.” Meredith Whitaker et al., “AI Now Report 2018” (AI Now Institute, December 2018), https://ainowinstitute.org/AI_Now_2018_Report.pdf. See also Lisa Feldman Barrett et al., “Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements,” Psychological Science in the Public Interest 20, no. 1 (July 17, 2019): 1–68, https://doi.org/10.1177/1529100619832930.

[9] See, for example, Ruth Leys, “How Did Fear Become a Scientific Object and What Kind of Object Is It?”, Representations 110, no. 1 (May 2010): 66–104, https://doi.org/10.1525/rep.2010.110.1.66. Leys has offered a number of critiques of Ekman’s research program, most recently in Ruth Leys, The Ascent of Affect: Genealogy and Critique (Chicago and London: University of Chicago Press, 2017). See also Lisa Feldman Barrett, “Are Emotions Natural Kinds?”, Perspectives on Psychological Science 1, no. 1 (March 2006): 28–58, https://doi.org/10.1111/j.1745-6916.2006.00003.x; Erika H. Siegel et al., “Emotion Fingerprints or Emotion Populations? A Meta-Analytic Investigation of Autonomic Features of Emotion Categories.,” Psychological Bulletin, 20180201, https://doi.org/10.1037/bul0000128.

[10] Fei-Fei Li, as quoted in Dave Gershgorn, “The Data That Transformed AI Research—and Possibly the World,” Quartz, July 26, 2017, https://qz.com/1034972/the-data-that-changed-the-direction-of-ai-research-and-possibly-the-world/. Emphasis added.

[11] John Markoff, “Seeking a Better Way to Find Web Images,” The New York Times, November 19, 2012, sec. Science, https://www.nytimes.com/2012/11/20/science/for-web-images-creating-new-technology-to-seek-and-find.html.

[12] Their paper can be found here: Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems 25, ed. F. Pereira et al. (Curran Associates, Inc., 2012), 1097–1105, http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf.

[13] Released in the mid-1980s, this lexical database for the English language can be seen as a thesaurus that defines and groups English words into synsets, i.e., sets of synonyms. https://wordnet.princeton.edu This project takes place in a broader history of computational linguistics and natural-language processing (NLP), which developed during the same period. This subfield aims at programming computers to process and analyze large amounts of natural language data, using machine-learning algorithms.

[14] See Geoffrey C. Bowker and Susan Leigh Star, Sorting Things Out: Classification and Its Consequences, First paperback edition, Inside Technology (Cambridge, Massachusetts and London: MIT Press, 2000): 44, 107; Anja Bechmann and Geoffrey C. Bowker, “Unsupervised by Any Other Name: Hidden Layers of Knowledge Production in Artificial Intelligence on Social Media,” Big Data & Society 6, no. 1 (January 2019): 205395171881956, https://doi.org/10.1177/2053951718819569.

[15] These are some of the categories that have now been entirely deleted from ImageNet as of January 24, 2019.

[16] For an account of the politics of classification in the Library of Congress, see Sanford Berman, Prejudices and Antipathies: A Tract on the LC Subject Heads Concerning People (Metuchen, NJ: Scarecrow Press, 1971).

[17] We’re drawing in part here on the work of George Lakoff in Women, Fire, and Dangerous Things: What Categories Reveal about the Mind (Chicago: University of Chicago Press, 2012).

[18] See Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, “Imagenet: A Large-Scale Hierarchical Image Database” In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255.

[19] Quoted in Allan Sekula, “The Body and the Archive,” October 39 (1986): 3–64, https://doi.org/10.2307/778312.

[20] Ibid; for a broader discussion of objectivity, scientific judgment, and a more nuanced take on photography’s role in it, see Lorraine Daston and Peter Galison, Objectivity, Paperback ed. (New York: Zone Books, 2010).

[21] “UTKFace - Aicip,” accessed August 28, 2019, http://aicip.eecs.utk.edu/wiki/UTKFace.

[22] See Paul N. Edwards and Gabrielle Hecht, “History and the Technopolitics of Identity: The Case of Apartheid South Africa,” Journal of Southern African Studies 36, no. 3 (September 2010): 619–39, https://doi.org/10.1080/03057070.2010.507568. Earlier classifications used in the 1950 Population Act and Group Areas Act used four classes: “Europeans, Asiatics, persons of mixed race or coloureds, and ‘natives’ or pure-blooded individuals of the Bantu race” (Bowker and Star, 197). Black South Africans were required to carry pass books and could not, for example, spend more than 72 hours in a white area without permission from the government for a work contract (198).

[23] Bowker and Star, 208.

[24] See F. James Davis, Who Is Black? One Nation’s Definition, 10th anniversary ed. (University Park, PA: Pennsylvania State University Press, 2001).

[25] See Joy Buolamwini and Timnit Gebru, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” in Conference on Fairness, Accountability, and Transparency, 2018, 77–91, http://proceedings.mlr.press/v81/buolamwini18a.html.

[26] Michele Merler et al., “Diversity in Faces,” ArXiv:1901.10436 [Cs], January 29, 2019, http://arxiv.org/abs/1901.10436.

[27] “Webscope | Yahoo Labs,” accessed August 28, 2019, https://webscope.sandbox.yahoo.com/catalog.php?datatype=i&did=67&guccounter=1.

[28] Olivia Solon, “Facial Recognition’s ‘Dirty Little Secret’: Millions of Online Photos Scraped without Consent,” March 12, 2019, https://www.nbcnews.com/tech/internet/facial-recognition-s-dirty-little-secret-millions-online-photos-scraped-n981921.

[29] Stephen Jay Gould, The Mismeasure of Man, revised and expanded (New York: Norton, 1996). The approach of measuring intelligence based on skull size was prevalent across Europe and the US. For example, in France, Paul Broca and Gustave Le Bon developed the approach of measuring intelligence based on skull size. See Paul Broca, “Sur le crâne de Schiller et sur l’indice cubique des crânes,” Bulletin de la Société d’anthropologie de Paris, I° Série, t. 5, fasc. 1, p. 253-260, 1864. Gustave Le Bon, L’homme et les sociétés. Leurs origines et leur développement (Paris: Edition J. Rothschild, 1881). In Nazi Germany, the “anthropologist” Eva Justin wrote about Sinti and Roma people, based on anthropometric and skull measurements. See Eva Justin, Lebensschicksale artfremd erzogener Zigeunerkinder und ihrer Nachkommen [Biographical destinies of Gypsy children and their offspring who were educated in a manner inappropriate for their species], doctoral dissertation, Friedrich-Wilhelms-Universität Berlin, 1943.

[30] “Figure Eight | The Essential High-Quality Data Annotation Platform,” Figure Eight, accessed August 28, 2019, https://www.figure-eight.com/.

[31] The authors made a backup of the ImageNet dataset prior to much of its deletion.

[32] Their “MegaPixels” project is here: https://megapixels.cc/

[33] Jake Satisky, “A Duke Study Recorded Thousands of Students’ Faces. Now They’re Being Used All over the World,” The Chronicle, June 12, 2019, https://www.dukechronicle.com/article/2019/06/duke-university-facial-recognition-data-set-study-surveillance-video-students-china-uyghur.

[34] “2nd Unconstrained Face Detection and Open Set Recognition Challenge,” accessed August 28, 2019, https://vast.uccs.edu/Opensetface/; Russell Stewart, Brainwash Dataset (Stanford Digital Repository, 2015), https://purl.stanford.edu/sx925dc9385.

[35] Melissa Locker, “Microsoft, Duke, and Stanford Quietly Delete Databases with Millions of Faces,” Fast Company, June 6, 2019, https://www.fastcompany.com/90360490/ms-celeb-microsoft-deletes-10m-faces-from-face-database.

[36] Madhumita Murgia, “Who’s Using Your Face? The Ugly Truth about Facial Recognition,” Financial Times, April 19, 2019, https://www.ft.com/content/cf19b956-60a2-11e9-b285-3acd5d43599e.

[37] Locker, “Microsoft, Duke, and Stanford Quietly Delete Databases”

[38] Full video here: Amarjot Singh, Eye in the Sky: Real-Time Drone Surveillance System (DSS) for Violent Individuals Identification, 2018, https://www.youtube.com/watch?time_continue=1&v=zYypJPJipYc.

[39] Steven Melendez, “Watch This Drone Use AI to Spot Violence in Crowds from the Sky,” Fast Company, June 6, 2018, https://www.fastcompany.com/40581669/watch-this-drone-use-ai-to-spot-violence-from-the-sky.

[40] James Vincent, “Drones Taught to Spot Violent Behavior in Crowds Using AI,” The Verge, June 6, 2018, https://www.theverge.com/2018/6/6/17433482/ai-automated-surveillance-drones-spot-violent-behavior-crowds.

[41] Ibid.

[42] Gould, The Mismeasure of Man, 140.