The unremarked optimum: whiteness, optimization, and control in the database revolution

ABSTRACT

The 1970s saw major transformations in how computerized databases were conceived, developed, and designed. Part of a broader shift in how software applications were developed, these transformations—sometimes referred to as “the database revolution”—introduced new and then-novel approaches to structuring and arranging digital data, optimizing them for usability and convenience. At the same time, however, the rhetoric of innovation and revolution surrounding this moment in database development obscures the ways it helped concentrate and extend particular kinds of racialized power and, in particular, whiteness (i.e., those norms and values congenial to the reproduction of white racial dominance and the subjugation of blackness). In this article, we revisit key works of the database revolution to show how they encoded whiteness as a kind of unremarked optimum, in both implicit and explicit ways. Finally, we argue that these developments helped to codify and extend a kind of “willful ignorance” that, as scholars of epistemology and justice have shown, is central to the preservation and reproduction of whiteness.

KEYWORDS:

• whiteness
• optimization
• race
• data
• database revolution

Acknowledgements

The authors would like to thank the Guest Editors of this themed issue, as well as the anonymous reviewers, for feedback and insight that contributed to the development of this work.

Notes

1 David Golumbia, The Cultural Logic of Computation (Cambridge, MA: Harvard University Press, 2009), 154.

2 Here, we note a distinction between “blackness” and “Blackness.” For present purposes, “blackness” refers to a designated position within the social order, while “Blackness” refers to the lives, cultures, histories, and identities of Black people. This distinction follows Robert Gooding-Williams’s work distinguishing “being black” from “being a black person,” where the former is “to be subject to a practice of racial classification that counts one as black” (24) while the latter is when “one begins to make choices, to formulate plans, to express concerns. . . in light of one’s identification of oneself as black” (23). The present analysis is largely confined to the former, especially against and within social orders structured by white normativity and violence. See Robert Gooding-Williams, “Race, Multiculturalism, and Democracy,” Constellations 5, no. 1 (1998): 18–41.

3 Anna Lauren Hoffmann, “Where Fairness Fails: Data, Algorithms, and the Limits of Antidiscrimination Discourse,” Information, Communication & Society 22, no. 7 (2019): 910.

4 OED Online, s.v. “optimize, v.,” Oxford University Press, accessed September 2020 (accessed September 2020).

5 Singiresu S. Rao, Engineering Optimization: Theory and Practice, 5th ed. (West Sussex, U.K.: Wiley, 2019), 1.

6 Michel Foucault, The History of Sexuality: Volume I: An Introduction, trans. Robert Hurley (New York: Vintage, 1978), 24.

7 Theo van Leeuwen, Discourse and Practice: New Tools for Critical Analysis (Oxford: Oxford University Press, 2008).

8 Frank B. Wilderson III, Red, White, and Black: Cinema and the Structure of U.S. Antagonisms (Durham, NC: Duke University Press, 2010); Hortense J. Spillers, “Mama’s Baby, Papa’s Maybe: An American Grammar Book,” Diacritics 17, no. 2 (1987): 70.

9 Spillers, “Mama’s Baby, Papa’s Maybe,” 75.

10 Ibid., 72.

11 Charles W. Mills, Blackness Visible: Essays on Philosophy and Race (Ithaca, NY: Cornell University Press, 1998), 10.

12 Barbara J. Flagg, “‘Was Blind, but Now I See': White Race Consciousness and the Requirement of Discriminatory Intent,” Michigan Law Review 91, no. 5 (1993): 957.

13 Frantz Fanon, Black Skin, White Masks, trans. Charles Lam Markmann (London: Pluto Press, 1986).

14 David R. Roediger, The Wages of Whiteness: Race and the Making of the American Working Class (New York: Verso, 1991), 165–66. Of course, this is also true of other non-white groups. For example, Edward Said’s examination of Orientalism demonstrated how Western conceptions of the Middle East and Asia served to construct “the orient” as Europe’s Other. See Edward Said, Orientalism (New York: Pantheon, 1978). For present purposes, however, we will be focusing on blackness, whiteness, and racial formations of the United States.

15 George Yancy, Look, a White! Philosophical Essays on Whiteness (Philadelphia, PA: Temple University Press, 2012), 106.

16 Simone Browne, Dark Matters: On the Surveillance of Blackness (Durham, NC: Duke University Press, 2015), 16.

17 Ibid., 17.

18 Ibid. 26–27; see also Lewis Gordon, “Is the Human a Teleological Suspension of Man? Phenomenological Exploration of Sylvia Wynter’s Fanonian and Biodicean Reflections,” in After Man, Towards the Human: Critical Essays on the Thought of Sylvia Winter, ed. Anthony Bogues (Kingston, Jamaica: Ian Randle Publishers, 2006).

19 David Lloyd, “Race Under Representation,” Oxford Literary Review 13, nos. 1/2 (1991): 74.

20 Alka Menon, “Reconstructing Race and Gender in American Cosmetic Surgery,” Ethnic and Racial Studies 40, no. 4 (2017): 597, 604.

21 Thomas Nakayama and Robert L. Krizek, “Whiteness: A Strategic Rhetoric,” Quarterly Journal of Speech 81, no. 3 (1995): 297.

22 Benjamin, Ruha. Race after Technology: Abolitionist Tools for the New Jim Code (Medford, MA: John Wiley & Sons, 2019), 46.

23 André Brock, “Critical Technocultural Discourse Analysis,” New Media and Society 20, no. 3 (2016): 1012–30.

24 Ibid., 1027.

25 Robert W. Gehl and Sarah A. Bell, “Heterogeneous Software Engineering: Garmisch 1968, Microsoft Vista, and a Methodology for Software Studies,” Computational Culture 2 (September 28, 2012): http://computationalculture.net/heterogeneous-software-engineering-garmisch-1968-microsoft-vista-and-a-methodology-for-software-studies/.

26 Niels Kerssens, “The Database ‘Revolution’: The Technological and Cultural Origins of the Big-Data-Based Mindset in American Management, 1970s–1980s,” TMG Journal for Media History 21, no. 2 (2018): 7–29.

27 Tara McPherson, “U.S. Operating Systems at Mid-Century,” in Race After the Internet, ed. Peter Chow-White and Lisa Nakamura (London: Routledge, 2013), 27.

28 Edgar F. Codd, “A Relational Model of Data for Large Shared Data Banks,” Communications of the ACM 13, no. 6 (1970): 377 emphasis added.

29 See Thomas Haigh, “‘A Veritable Bucket of Facts’: Origins of the Data Base Management System,” ACM SIGMOD Record 35, no. 2 (2006): 33–49. Though now dominant, the relational model represents only one way that this technology could have evolved. See Paul Dourish, “No SQL: The Shifting Materialities of Database Technology,” Computational Culture, no. 4 (2014): http://computationalculture.net/no-sql-the-shifting-materialities-of-database-technology/.

30 Edgar F. Codd, “Normalized Data Base Structure: A Brief Tutorial,” in Proceedings of the 1971 ACM SIGFIDET (Now SIGMOD) Workshop on Data Description, Access and Control (New York: Association for Computing Machinery, 1971), 3, https://doi.org/10.1145/1734714.1734716.

31 Kerssens, “The Database ‘Revolution,’” 10.

32 Codd, “Normalized Data Base Structure.”

33 International Organization for Standardization, Technical Report 9007: Information Processing Systems—Concepts and Terminology for the Conceptual Schema and the Information Base (n.p.: 1987), https://www.iso.org/standard/16549.html.

34 William Kent and Steve Hoberman, Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World, 3rd ed. (Westfield, NJ: Technics Publications, 2012), 28; Jean-Raymond Abrial, Data Semantics (Grenoble: Université Scientifique et Médicale, 1974).

35 Abrial, Data Semantics.

36 Abrial, Data Semantics, 3–10.

37 Abrial’s text includes assigning and retrieving the “sex” of people in the format sex(x) = masculine. The word “feminine” does not appear.

38 Brian Beaton, “How to Respond to Data Science: Early Data Criticism by Lionel Trilling,” Information & Culture 51, no. 3 (2016): 365–66.

39 Sandra Littletree, Miranda Belarde-Lewis, and Marisa Duarte, “Centering Relationality: A Conceptual Model to Advance Indigenous Knowledge Organization Practices,” Knowledge Organization 47, no. 5 (2020): 410–26. For more on the relationship between Black and Indigenous studies, see Tiffany Lethabo King, The Black Shoals: Offshore Formations of Black and Native studies (Durham, NC: Duke University Press, 2019).

40 Peter Pin-Shan Chen, “The Entity-Relationship Model—toward a Unified View of Data,” ACM Transactions on Database Systems 1, no. 1 (1976): 10.

41 Peter Pin-Shan Chen, “The Entity-Relationship Model: A Basis for the Enterprise View of Data,” Proceedings of the June 13–16, 1977, National Computer Conference (1977): 77–84.

42 Chen, “The Entity-Relationship Model—toward a Unified View of Data,” 14.

43 Ibid., 10n1.

44 McPherson, “U.S. Operating Systems at Mid-Century,” 27.

45 Chris Gilliard, “Friction-Free Racism” Real Life, October 15, 2018, https://reallifemag.com/friction-free-racism/.

46 Though not a primary focus of this paper, it is worth noting that this period also saw heightened political and social scrutiny of databases, especially in the United States. Driven by concerns over privacy and due process, policy and other discussions often elided issues of race and ethnicity or actively exhibited racist attitudes. For example, in 1966 (just two years after passage of the Civil Rights Act) congressional hearings on computers and privacy were framed in white hegemonic terms, only referencing African Americans when noting the potential utility of computation to address pressing problems such as “urban blight, the transportation tangle, the integration of the Negro into American society, and the continuing spread of crime” (Committee on Government Operations, The Computer and Invasion of Privacy [Washington, DC: GPO, 1966], 303). Five years later, discussions of the influential Secretary’s Advisory Committee on Automated Personal Data Systems showed committee members preoccupied with threats of Stasi-style secret police while also advocating for surveillance to prevent “Mexican immigrants” from obtaining certain social services (April 17–18, 1972, transcripts at Chris Hoofnagle, “Archive of the Meetings of the Secretary’s Advisory Committee on Automated Personal Data Systems (SACAPDS): The Origin of Fair Information Practices,” Berkeley Center for Law and Technology, 2015, https://www.law.berkeley.edu/research/bclt/research/privacy-at-bclt/archive-of-the-meetings-of-the-secretarys-advisory-committee-on-automated-personal-data-systems-sacapds/).

47 McPherson, “U.S. Operating Systems at Mid-Century.”

48 Eduardo Bonilla-Silva, Racism without Racists: Color-Blind Racism and Racial Inequality in Contemporary America, 3rd ed. (New York: Rowman & Littlefield, 2010).

49 Thomas J. Hrach, “An Incitement to Riot: Television’s Role in the Civil Disorders in the Summer of 1967,” Journalism History 37, no. 3 (2011): 163–71; Charlton D. McIlwain, Black Software: The Internet and Racial Justice, from the AfroNet to Black Lives Matter (Oxford: Oxford University Press, 2019).

50 Oliver Belcher, “Sensing, Territory, Population: Computation, Embodied Sensors, and Hamlet Control in the Vietnam War,” Security Dialogues 50, no. 5 (2019): 416–36; Joy Rohde, “Pax Technologica: Computers, International Affairs, and Human Reason in the Cold War,” Isis 108, no. 4 (2017): 792–813.

51 Gilliard, “Friction-Free Racism.”

52 It is worth noting, that this move was not exclusive to Abrial (though his work was particularly influential). For example, earlier work by Woody Bledsoe—considered by many to be the “father” of facial recognition technology—advanced similar formalisms in the context of phrenological research using computer vision to detect whether a person is white or Black. As with Abrial’s work, Bledsoe’s models uncritically took up whiteness as a baseline, as they were explicitly designed to detect deviance from the (white) norm (an explicit codification of the “white” / “not-white” binary). See Woody Bledsoe letter to Dr. Samuel Koslov, ARPA, March 25, 1965.

53 Browne, Dark Matters, 16–17.

54 Wendy Hui Kyong Chun, Programmed Visions: Software and Memory (Cambridge, MA: MIT Press, 2013).

55 Chen, “The Entity-Relationship Model—toward a Unified View of Data,” 11.

56 Golumbia, The Cultural Logic of Computation, 191.

57 Erinn Gilson, “Vulnerability, Ignorance, and Oppression,” Hypatia 26, no. 2 (2011): 308–32.

58 Sara Ahmed, “A Phenomenology of Whiteness,” Feminist Theory 8, no. 2 (2007): 149–68.

59 Charles W. Mills, “White Ignorance,” in Race and Epistemologies of Ignorance, ed. Shannon Sullivan and Nancy Tuana (Albany: State University of New York Press, 2007), 28.

60 Gilson, “Vulnerability, Ignorance, and Oppression.”

61 Elizabeth V. Spelman, “Managing Ignorance,” in Race and Epistemologies of Ignorance, ed. Shannon Sullivan and Nancy Tuana (Albany: State University of New York Press, 2007), 119–31.

62 Chun, Control and Freedom, 30.

63 Edgar F. Codd, “Relational Database: A Practical Foundation for Productivity,” in Readings in Artificial Intelligence and Databases, ed. John Mylopolous and Michael Brodie (San Mateo, CA: Morgan Kaufmann, 1989), 67.

64 Ibid., 68.

65 Jessica Marie Johnson, “Markup Bodies: Black [Life] Studies and Slavery [Death] Studies at the Digital Crossroads,” Social Text 36, no. 4 (137) (2018): 58.