By DiacriticaOwn work, CC BY-SA 3.0, Link

To be a person of color, especially Black, is to be both at risk and a risk, by design.

There is a segment in the satirical HBO show Random Acts of Flyness where a pregnant black woman enters a hospital lobby with a male partner and approaches a white man at a service desk. She explains that she has been diagnosed with preeclampsia and is looking for the lab. An infographic appears, titled “The Color of Risk,” which shows that black women in the US have a mortality of almost 40 per 100,000 live births, more than twice that of any other racial group, and three to four times that of white women. A voiceover says, “The study has shown that these inequities are present at conception…”1

Preeclampsia is a “pregnancy complication characterized by high blood pressure.” The nonprofit Mayo Clinic lists race as a risk factor for both preeclampsia2 and high blood pressure3, with Black people having it worse than any other race. Race is often used as a factor for health risks.

A former professor of mine, feminist philosopher, and psychoanalytic theorist Teresa Brennan was a leading articulator of the field of affect. In Transmission of Affect, she writes, “Rather than a generational line of inheritance (the vertical line of history), the transmission of affect, conceptually, presupposes a horizontal line of transmission: the line of the heart.”4

She explains that what someone feels about another is transmitted via smell hormones.

Pheromones act as direction-givers which, as molecules, traverse the physical space between one subject and another, and factor in or determine the direction taken by the subject who inhales or absorbs them….Influencing the subject’s intentionality means influencing the subjects will or agency in ways that make it unfree (following the psychoanalytic definition of freedom.)”5

Brennan’s brilliance is in noticing that people who are thought less of in society experience consistent negative affect that accumulates. She explains, “All the negative affects result from a disabling comparison. The notion of comparison requires another, a yardstick separate from the self….the price of this orientation is a loss in the energy hitherto available to it.”6 She calls these negative affects “passionate judgments,” and notes that they often offer the “other” a negative self-image that is “at odds with the life drive.”7

Brennan closes the loop when she observes that the flip side of negative comparisons is a “narcissistic sense of superiority…and the imaginary threats to superiority accompanying it (always comparative threats).”8

In Thinking in Systems, a primer on systems thinking, Donella H. Meadows includes misperception of risk as one of the key factors in misunderstanding systems. She writes, “We misperceive risk, assuming that some things are much more dangerous than they really are and others much less.”9

Frontex is the European border and coast guard agency. It was founded in 2005 after the EU grew from 15 to 25 members, with the membership of the formerly Eastern Bloc countries. The organization has grown powerful over the years as it responds to increased migration from the Middle East and northern Africa, the result of conflict and famine. It has been criticized by NGOs for denying asylum to refugees, which is a claim that is protected under the United Nations 1951 Refugee Convention.

An article in the journal Critical Studies on Security titled “The gendered and racialized politics of risk analysis. The case of Frontex” finds that “risk analysis has become a central security practice.”10 The authors, Saskia Stachowitsch and Julia Sachseder, deconstruct the border narrative and find that risk analysis is “not a neutral and objective assessment of a reality outside of itself, but co-constitutive.” It creates its own reality by defining who and what is considered a risk, who and what needs to be defended, and which solutions are plausible or even necessary. Underlying it all is Frontex’s ability to collect data and make meaning from it.

Europe, with its widespread history of colonization, thereby rationalizes what the authors call “border security regimes.” But risk analysis is rooted in colonial knowledge practices. Like people of color and black people in particular, these refugees are constituted as both at risk and a risk. While they are fleeing dire conditions, what is emphasized is the migrant’s “unknownness,” which could, at worst, hide danger (there may be criminals or terrorists), and, at best, contain different values and norms.

The authors add:

These notions construct Europe and the imagined homogeneous “Europeans” as the principal subjects of protection, while those who suffer from disease or as an effect of postcolonial power are ignored. In the tradition of colonial humanitarianism, the report does not consider the psychological, mental, and emotional health of potentially traumatized refugees, whilst reproducing colonial understandings of Europe’s “Other” as unclean, diseased, and un-modern and linked to the threat of exploitation and overstretch.

The categorization that undergirds risk analysis produces constructions of race. All of this plays out against the background of the EU’s welfare state crisis and raises “the question of who has the right to social provisions.” In this way, access to basic resources becomes racialized.

Risk analysis is a sensemaking security practice of the West designed to protect the elite and distribute resources unevenly, in a way that amplifies inequities. The authors conclude that it is very difficult to challenge this type of widespread discrimination because it is tied up with notions of identity—the self as rational and good and the “other” as an unknowable, unworthy risk.

To understand how the braiding of risk and race came to be, it helps to know that the concept of risk as a discourse has been developed alongside the concept of race, beginning with white colonial settling of the United States. The discourse of risk was developed in the private sector, moved into the public sector, and can easily be seen in the practices of the nonprofit sector. (In a recent New York Times opinion article, Vanessa Daniel, Executive Director of Groundswell Fund, notes that women of color receive less than one percent of funding and a lot of that boils down to a perception of risk.11) In his forthcoming book, Loaded Dice: Race & Risk in the United States, Benjamin Wiggins traces the history of risk analysis in the US and, with the advent of big data, our development into a risk society.

The concept of risk in the US begins with the shipping industry’s practice of insuring human cargo in the trans-Atlantic slave trade. He writes (in reference to Captain Peleg Clarke, who, in 1776, arrives on the slave ship Thames to a conflict between the Danes and the Dutch at Accra),

In referring to his premium as a way to purchase protection against both the risk of mortality and insurrection, Clarke’s account reveals that while maritime slave insurance offered security against the loss of the ship’s “property,” this property was also human and in that humanity was both at risk and a risk.12

Wiggins notes that while many have spoken about the key role of race in risk assessment, “no study has fully captured the disturbingly sustained and multifaceted use of race as a significant variable in actuarial science—the probabilistic mathematics of risk assessment.”13 As his own outline reveals, the “constructions of people of color as risk compounds the burden of risk that they must bear.”14 Loaded Dice investigates the three most significant sites for race and actuarial science—insurance, criminology, and housing.

Insurance

Prudential was the first life insurer in the US to insure people of color directly, as people. It was different from other insurers of the time in that it sought out people other insurers considered “too dangerous” to insure, mostly laborers, and at first, because they had no policy against it, Black people.15 Given the opportunity, “blacks purchased life policies in droves.”16 Wiggins thoughtfully notes that insurance gave Blacks “a small bit of confidence in a social world that systematically deprived them of security.”17

When Prudential realized that a significant number of its policyholders were Black, it conducted a statistical survey of Black mortality and found that it was 50 percent higher than that of whites. This should not come as a surprise, since Black people “had recently lived in the miserable conditions of chattel slavery and…were now living in the equally miserable conditions of debt peonage.”18 The study, however, did not take this context into account. Removing any relationality from the decision,

Prudential made the decision to drop benefits by one-third for black adults and increase premiums for black infants from three cents per week to five. However, company executives felt that even the benefit drop and premium increase was not enough to dissuade black business….And weeks later, Blanchard’s regional office began to simply refuse premiums from blacks, refund them, and lapse their policies…the company adopted a position of non-solicitation of people of color along with a penalization of any agent who might bring the company such risks.19

This decision to insure Black people at differential rates signified “an emergent entanglement of actuarial science and race.”20 Beneficiaries of color would only receive two-thirds as much as white ones, and most without even knowing it. Wiggins observes, “If reports from Prudential, Metropolitan, John Hancock, and the American Experience Table of Mortality on black mortality are to be trusted, then the justification of race as a risk factor held significant actuarial value.”21 So, in addition to extracting life from people of color, especially Blacks, white insurers extracted further profit from that loss.

Prudential hired Frederick Hoffman, who lacked any formal trading in statistics, as chief actuary. In 1896, he published Race Traits and Tendencies of the American Negro, in which he “sought to prove the inherent riskiness of the American Negro” with the circular logic that “because most African-Americans’ vital measures had either been stagnated or declined since the abolition of slavery, their traits and tendencies were unalterable and the race unworthy of assistance,” since “the negro shows the least power of resistance in the struggle for life” and “the race is doomed for extinction.”22

But Hoffman’s lack wasn’t limited to statistics. In a complete nonsensical reversal to his determination that Blacks were inherently risky because of the toll of slavery and debt peonage, he also concluded that “racial disparities in vital statistics were not the result of ‘conditions of life’ but rather the product of innate traits and tendencies in people of color, principally in those of African descent.”23 And, in the ultimate reversal, Hoffman “concluded his case for the inherent riskiness of African Americans with a discussion of their economic productivity” arguing that this class of people, upon whom the wealth of the US was built, was unproductive and lazy.24 Since, of course, they could not, in Hoffman’s view, be resisting by withholding their labor.

Wiggins rightly connects Hoffman’s arguments to philosopher Michel Foucault’s concept of power as biopolitics, where the division of humans along lines of difference and attribution of value, or lack thereof, lead to policies that kill or otherwise dispose of those labeled inferior. Hoffman made the effects of racism quantifiable and profitable. And justified, since they were “doomed to extinction.” All of these are social constructions made physical (or physical desires made social) in the construction of a dominant class.

Criminology

The use of race to discriminate risk “became portable, and was soon adapted by criminologists” at the turn of the century.25 Wiggins writes, “With the decline of branding, police—especially in urban centers—began to seek alternative methods for tracking criminal histories.”26 In 1895, Illinois’s Sentencing and Parole Act mandated the collection of data on prisoners, including race. The importation of the Bertillon System of Criminal Identification from Paris in the 1880s provided a foundation. It offered a framework for mapping the prisoner body. It was quickly implemented in Illinois and spread throughout the US. The Bertillon System did not include race. But by 1896, just one year later, race was included per “lobbying from English officials.”27

In the 1920s, Illinois moved from operationalizing its data to advancing “a data-driven system for the assessment of sentencing and parole risks.”28 In fact, the renowned “Chicago School” of sociology is known for refocusing the field on prediction and intervention. One of its innovations was looking to the past for predictive factors. Chicago School sociologist Ernest Burgess was charged with the identification of the state’s predictive factors. Racial categories came to constitute three of Burgess’s 21 factors.

However, like Hoffman before him, in spite of looking back at history to identify factors, Burgess made no effort “to control for racial inequities in policing and society generally,” when he concluded that Blacks were a bad parole risk.29 Not even the notorious Black Codes, which “in essence criminalized Black existence in the postbellum United States South.”30 Burgess’s racial categories were in use until 1954, when his student, Daniel Glaser, replaced it with a seven-factor version that “spelled the end of the direct factoring of race in sentencing and parole formulas.”31 However, criminal history, which emerged as the factor with the most predictive value, was a proxy for race.

Housing

The third major area for the development of the discourse of risk as race is home ownership. Wiggins argues that though many look to the stock market as the source of the Depression Era in the US, it was, in fact, kicked off by an increase in mortgage foreclosures in 1926. President Hoover made efforts to address what Wiggins calls the “full-blown housing crisis,” including the passing of acts and creation of a federal lending institution, but foreclosures kept rising through 1933, “exceeding a quarter-million for the first time in the country’s history.”32 So, in 1934 Congress, backed by President Franklin Delano Roosevelt, passed the National Housing Act, which led to the creation of a national mortgage association, which eventually became Fannie Mae.

Wiggins notes that, “of the many causes influencing [the] 1920s housing bubble, an utter lack of records and analysis about the trends of mortgage loans ranks high.”33 At the time, many loans were based on subjective judgments, such as relationships and local knowledge. Thus, statistical research became a core approach in the new agency. Without a history for setting standards, the FHA [Federal Housing Administration] turned to experts in risk rating, “a handful of men from the field of real estate valuation.”34

The FHA hired University of Michigan’s Ernest Fisher and Prudential’s Frederick Babcock, both of whom were linked to prominent economist Richard T. Ely, who “borrowed a race-inflected actuarial thinking from the turn of the century insurance industry and infused it into his early twentieth century valuation of real estate.”35 Wiggins finds, unsurprisingly, that “in his projection of value, Ely constructed people of color and immigrants as a risk and encouraged investors to seek protections to fortify their property against infiltration from such undesirable groups.”36 Ely advocated for national housing policy that intervenes in the market to check the pursuit of profit and ensure the construction of segregation.37 Much of this segregation was accomplished through “improvement associations” and zoning.

Through clauses that took into account not only physical, but social factors in determining value, people of color were effectively barred from FHA-backed loans in white neighborhoods. Wiggins notes, “And by precluding most people of color access to the wealth generator that was the single-family home in the midst of country’s depressed economy, the FHA deeply entrenched the racial wealth gap.”38

The effects on the Black community were felt early on, and by 1938 the NAACP suspected that racial discrimination was federal housing policy. When it finally secured a copy of the FHAs Underwriting Manual from a field office, it uncovered “the stunning depth of the manual’s factorization of race in its formula.”39 When it pressed for change, taking the issue all the way to President Roosevelt, “like life insurers and criminologists before them, the FHA argued that their policies were not racist per se, but rather that their statistical assessments of risk simply reflected an objective reality about the riskiness of people of color and undesirable nationality groups.”40

The manual retained its racialized formula until 1947, when the FHA complied with a Supreme Court ruling on the unenforceability of racially restrictive covenants. However, by this point implicit measures were already in practice—race had become an implicit risk factor. Babcock summarized it well when he boasted that, in spite of all the resistance, “emphasis is now placed on risk instead of value” and people of color were constructed as “a risk not worth taking.”41

The Kirwan Institute’s Challenging Race as Risk reports that “housing and credit can influence our daily lives: the better one’s access to safe, affordable housing, the better one’s outcomes tend to be along a range of indicators of individual, family, and community well-being.”42 It rightly calls the “deeply rooted association between race and risk,” “opportunity segregation.”43 Further, “individuals internalize the racial boundaries of the housing market.”44 Since intergroup contact is the key factor in mutual understanding among racial groups, housing and neighborhood segregation is the foundation of societal segregation.

The use of data to justify and ensure the economic, political, and social privilege of whites reveals, as Wiggins asserts, that “race is not a category of enlightened reason, but rather a technology of power…it created an othered group and imbued this othered group with a negative connotation.”45 Though the aforementioned architects of racists policies sought to take history and social context into account in devaluing people of color, especially Black people, they simultaneously erased that history and their own role in creating and benefitting from those same conditions.

German sociologist Ulrich Beck argues that risk has become “the fundamental shaping force of modernity.”46 In Risk Society: Towards a New Modernity, he writes that the main concern of institutions in late modernity is “how can the risks and hazards systematically produced as a part of modernization be prevented, minimized, dramatized, or channeled?”47 Further, he astutely observes that in, decision making, “a concern for security” has replaced “a concern for freedom.”48

The buzzwords of modern life—“big data, algorithms, artificial intelligence, and machine learning”—descend from the practice of statistical risk assessment.49 Wiggins writes,

Mortality tables were big data before Big Data. Algorithms are actuarial calculations by another name. And the practice of consistently refining formulas as new information becomes available—that Bayesian principle that undergirds most artificial intelligence and machine learning—was manually done by the hands of clerks for over a century before it became the task of computers.50

He finds that today, “the factors used to discriminate risk largely [evade] regulation.”51 Much statistical assessments of risk today fall under the protection of intellectual property. And the practice now largely uses proxies for race.

In response, in 2014, a small team of computer scientists founded the Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) workshop to ensure nondiscrimination in machine-learning-based decision making. They created two documents to guide ethical use and development of these, “Principles for Accountable Algorithms” and “Social Impact Statement for Algorithms.” They include five demands for creators of machine-learning algorithms:

  1. “Creators take responsibility for their design of an algorithm and any biases contained within it”
  2. “Creators explain their algorithm’s processes to end-users in non-technical language”
  3. “Creators identify and articulate error and uncertainty in their algorithm”
  4. “Creators enable third parties to probe, understand, and review the behavior of their algorithm as it evolves”
  5. “Creators ensure that their algorithm does not create a discriminatory impact across key demographic characteristics such as race or sex”52

Watch groups have taken the lead in uncovering these biases. The 2016 ProPublica report “Machine Bias,” for example, describes how the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) was using data that was acting as a proxy for race. The 137-question form includes the following race proxy questions.

  • “Based on the screener’s observation, is this person a suspected or admitted gang member?”
  • “How many of your friends/acquaintances have ever been arrested?”
  • “In your neighborhood, have some of your friends or family been crime victims?”
  • “Were you ever suspended or expelled from school?”
  • “How often do you have trouble paying bills?”

The ProPublica team found that the formula was “particularly likely to falsely flag black defendants as future criminals, wrongly labeling them this way at almost twice the rate of white defendants” and that “white defendants were mislabeled as low risk more often than black defendants.”53 Note also the power the administrator of the tool has in judging for these. This is compounded by the exponential rise of computing power. Wiggins concludes, “Despite such efforts, the challenges of resisting racial discrimination in risk assessment remain vast.”54

What can be done? Wiggins, interestingly, recommends that a way forward may be found in the beginning of actuarial science. He argues that we can shift the practice from constructing bias, to constructing fairness. He writes, “from its beginning, the mathematics of risk has held the potential to gain advantage or to distribute risk equally.”55 He uses the words equally and equitably interchangeably, and those of us who work with these concepts know that there is a big difference. To share risk equally is to take it out of context, to move forward as if a national history of subordination has not occurred. To share risk equitably is to account for a national history of not just subordination, but exploitation. It suggests fairness is reparations.

The Challenging Race as Risk report makes the following recommendations.

  1. From an equity perspective, ask “How would we define ‘desirable structure’ today? And, “How would this change the valuation process?”56
  1. “Uplift the importance of implicit bias during the development and implementation of any new initiative aimed to advance racial and economic equity.”57
  2. Seek to understand the “‘opportunity structure for discrimination’ within organizations that ‘allow or inhibit the expression of discriminatory tendencies.’”58
  3. “Remain cognizant of the fact that ‘rules and procedures are themselves subject to the influence of groups inside and outside the organization who “mobilize resources in a way that advances their interests.”’”59
  4. Explore “what institutional arrangements could mitigate the effects of implicit biases related to race and risk?”60
  5. Choose “venues for more intergroup contact” other than work. In other words, address “white residential homogeneity as the normative ‘background’ of our lives.”61

Our own organizations and homes are a great starting point for delinking race and risk.

Much effort went into making people of color, especially Black people, both at risk and a risk. It’s time to change that, by design.

Notes

  1. Random Acts of Flyness. HBO. Episode 5.
  2. https://www.mayoclinic.org/diseases-conditions/preeclampsia/symptoms-causes/syc-20355745
  3. https://www.mayoclinic.org/diseases-conditions/high-blood-pressure/symptoms-causes/syc-20373410
  4. Brennan, Teresa. The Transmission of Affect. Ithaca and London: Cornell University Press, 2004. 75.
  5. Ibid, 75-76.
  6. Ibid, 109.
  7. Ibid, 110.
  8. Ibid, 109.
  9. Meadows, Donella H. Thinking in Systems. White River Junction, VT: Chelsea Green Publishing, 2008. 107.
  10. https://www.tandfonline.com/doi/full/10.1080/21624887.2019.1644050
  11. https://www.nytimes.com/2019/11/19/opinion/philanthropy-black-women.html
  12. Wiggins, Benjamin. Loaded Dice: Race & Risk in the United States. “Introduction.” Oxford University Press, forthcoming.
  13. Ibid.
  14. Ibid.
  15. Ibid.
  16. Ibid.
  17. Ibid.
  18. Ibid.
  19. Ibid.
  20. Ibid.