Feminist Theory Week 8: Social Identities, Algorithmic Identities, & Trans Credit Reporting
After a few weeks off for lit review projects, I’m back blogging my way through the readings for my grad seminar on Feminist Theory. This week we read Linda Alcoff on social identities, John Cheney-Lippold on algorithmic identities, & Lars Mackenzie on trans credit reporting.
Social Identities vs Algorithmic Identities
Traditional social identities (race, gender, sexuality) use outward appearance as the basis for inferences about inner content, character, or quality. As Linda Martin Alcoff argues, “visible difference, which is materially present even if its meanings are not, can be used to signify or provide purported access to a subjectivity through observable, ‘natural’ attributes, to provide a window on the interiority of the self” (VI 192). An identity is a “social identity” when outward appearance itself is a sufficient basis for inferring/attributing someone’s “internal constitution.” Kant and Hegel, for example, inferred races’ defining characteristics from the physical geography of their ‘native’ territories. As Kant argues, “The bulging, raised area under the eyes and the half-closed squinting eyes themselves seem to guard this same part of the face partly against the parching cold of the air and partly against the light of the snow…This part of the face seems indeed to be so well arranged that it could just as well be viewed as the natural effect of the climate” (11). For Kant, geography determined physiology determined internal character. Similarly, stereotypes connect outward appearance to personality and (in)capability; your bodily morphology is a key to understanding if you’re good at math, dancing, caring, car repair, etc. As David Halperin argues, what makes “sexuality” different from “gender inversion” and other models for understanding same-sex object choice is that “sexuality” includes this move from outward features to “interior” life. Though we may identify people as, say, Cubs fans or vegans, we don’t usually use that identity as the basis for making inferences about their internal constitution. Social identities are defined by their dualist logic of interpretation or representation: the outer appearance is a signifier of otherwise imperceptible inner content.
Social identities use outer appearance and inner character to rank people in a supposedly objective hierarchy of fixed differences. So, social identities don’t just categorize individuals; they also organize the categories in relation to one another. Linda Alcoff argues that this system of classification or “episteme” is also visual: “Racism makes productive use of this look, using learned visual cues to demarcate and organize human kinds. Recall the suggestion from Goldberg and West that the genealogy of race itself emerged simultaneous to the ocularcentric tendencies of the Western episteme, in which the criterion for knowledge was classifiability, which in turn required visible difference” (VI 198). As Foucault shows in The Order of Things, classification, or the “taxonomic area of visibility” (OOT 149-50), was a system of fixed differences in hierarchal relation, and it relied on objectifying vision. Examples of such systems include tonal harmony (a hierarchical series of chord functions grounded in the fixed differences among overtone frequencies) and Kant’s and Hegel’s comparative geographies of Europe, Asia, and Africa, and the racial classifications that follow from them. As Al-Saji argues, objectifying vision’s exclusive focus on vertical, hierarchical difference obscures “lateral” relationality. Thus, social identities are “visual” not only because they classify individuals by outward visual appearance, but also because they organize group categories into a fixed hierarchy.
Because objectifying vision was central to modernity’s white supremacist, patriarchal, capitalist social order, Alcoff speculates that de-centering (objectifying) vision would in turn de-center white supremacy: “without the operation through sight, then, perhaps race would truly wither away,” and we would know people on the basis of “their subjective lives…and not merely to objective and arbitrary bodily features (VI 198). As sound studies scholars such as Jennifer Stoever have demonstrated, sound–what we might call a kind of objectifying listening–also performs the racializing work Alcoff attributes to sight, creating what Stoever calls a “listening ear” that polices the relationship between a body’s visual appearance and the sounds that come out of it. Eliminating our reliance on objectifying vision (or listening) doesn’t eliminate either white supremacy or identity-based social exclusion.
First, in neoliberal biopolitics, white supremacy, patriarchy, and other forms of post-identity based domination all work at sub- and supra-visible scales. Statistics can describe phenomena that are too minute (like gene frequency) and too massive (population-level phenomena like birth rate) for objectifying vision to perceive. As Jared Sexton argues, citing Paul Gilroy, “Our collective estrangement from anatomical scale has rendered the eye inadequate, if it ever was, ‘to the tasks of evaluation and description demanded’ by racial segregation’,” which are less focused on “comparative anatomy” and more tuned to “health status” (Sexton 233-4). Traditional social identities take the body’s visible external surface as their object: outward appearance is the basis on which individuals are subsumed under an identity category, and this outward appearance is a sufficient ground for making inferences about physiological and psychological interiority (women are weak, women are emotional, etc.). Biopolitical technologies treat the body as “an epiphenomenon of coded information…The skin may no longer be privileged as the threshold of identity” (Gilroy 196 cited in Sexton 234) because it indexes identity first to the relative normality of individual patterns of behavior (such as health status). Hidden to objectifying vision, a body’s specific vitality–its degree of liveliness–can be represented in what we can call, after Martha Rosler, “vital statistics.” Blood pressure, cholesterol level, IQ, these are all biopolitical measures of a body’s vitality, and they are both invisible to the “normal” human eye. Neoliberal biopolitics classifies individuals mathematically rather than visually. This change in method also alters what “identity” is to begin with.
Instead of perceiving identity as a (stereo)type that can be objectively seen, post-identity biopolitics perceives it as an emergent, dynamic phenomenon that as Cheney-Lippold shows, discovered through data mining. “The practice of finding patterns within the chaos of raw data” (Cheney-Lippold 169), data mining tunes into the same type of emergent phenomena that Cavarero conceptualizes through acoustic resonance and that neoliberal political economy describes in terms of homo economicus’s cost/benefit calculus. Data mining seeks predictable behaviors that advertisers, retailers, and law enforcement have identified as significant. Algorithmic identities take the phusis of homo economicus–predictable patterns of behavior–and make it the nomos of both individuals and groups.
Instead of using visible identity as the basis of inference about inner capacities, algorithmic identities use patterns of behavior to infer the boundaries of identity categories themselves. As Cheney-Lippold explains, a gender algorithm “can name X as male, [but] it can also develop what ‘male’ may come to be defined as online” (167). “Maleness” as a category includes whatever behaviors that are statistically correlated with reliably identified “male” profiles: “maleness” is whatever “males” do. This is a circular definition, and that’s treated as a feature not a bug. Because algorithms and predictive analytics can be recursively self-modifying, they situate identity categories in a mutually-adaptive relationship with (in)dividual data points and profiles. Instead of using disciplinary technologies to compel exact individual conformity to a static, categorical norm, algorithmic technologies seek to “modulate” both (in)dividual behavior and group definition so they synch up as efficiently as possible–that is, so they become maximally harmonious and consonant. For example, demanding conformity to one and only one feminine ideal is less profitable for Facebook than it is to tailor their ads to more accurately reflect my style of gender performance. Cheney-Lippold calls this process of mutual adaptation “modulation” (168). A type of “perpetual training” (169) of both us and the algorithms that monitor us and send us information, modulation compels us to temper ourselves by the scales set out by algorithmic capitalism, but it also re-tunes these algorithms to fall more efficiently in phase with the segments of the population it needs to control. Flexible, adaptable categories are what makes “soft biopower” soft: “restage[ing] the relationship that biopower has to its subjects” (174) at the meta- level, categories aren’t fixed, “hard,” or even independent of biopolitical management. In soft biopolitics, both individual identities and identity categories are statistically normalized.
Because modulation is a feedback loop of mutual renegotiation between the category and individual instances, algorithmic identities appear to “de-essentialize” (Cheney-Lippold 170) identity categories. Algorithmic gender isn’t essentialist because gender categories have no necessary properties are constantly open to reinterpretation. As long as an individual is sufficiently (“statistically” (Cheney-Lippold, 160)) masculine or feminine in their online behavior, they are that gender–regardless of their meatspace gender identity. Because gender is de-essentialized, it seems like an individual “choice” and not a biologically determined fact. Anyone, as long as they act and behave in the proper ways, can access the privileges partriarchy reserves for men. Privileged categories aren’t de-centered, just expanded a bit and made superficially more inclusive. This is part of what makes algorithmic identity “post-identity.”
Soft biopolitics produces traditional social identities and identity politics as the exception, as a backwards state of existence that post-identity society has outgrown. Compared to modulating identities and identity categories, traditional social identities are too rigid and non-adaptive; they are biopolitically unfit, dysgenic, regressive. Traditional identity politics appear old-fashioned and obsolete because they tie life chances to group membership rather than individual behavior–to the color of one’s skin, not the content of one’s character, so to speak. Soft biopolitics appears more progressive because it ties life chances to individual behavior. Modulation “predicts our lives as users by tethering the potential for alternative futures to our previous actions as users” (Cheney-Lippold 169). Your past patterns of behavior determine the opportunities offered you, and the resources you’re given to realize those opportunities. The algorithms you synch up with determine the kinds of opportunities and resources that will be directed your way, and the number of extra hoops you will need to jump through (or not) to be able to access them. Think about credit scores: your past payment and employment history determines your access to credit (and thus to housing, transportation, even medical care). Credit history determines the cost at which future opportunity comes–opportunities are technically open to all, but at a higher cost to those who fall out of phase with the most healthful, profitable, privileged algorithmic identities. Such algorithmic governmentality “configures life by tailoring its conditions of possibility” (Cheney-Lippold 169): the algorithms don’t tell you what to do (or not to do), but to open specific kinds of futures for you. They differentially distribute the background conditions in and through which individuals can then behave and make choices. Because oppressed groups’ choices are more severely constrained by their background conditions, modulation produces these populations as less flexible, adaptable, and rational than they “ought” to be, than is “normal.” In this type of soft biopolitics, the ability to (self-)modulate defines life as such. Thus, fixed or non-modulatory phenomena–like traditional social identities, or cyclical poverty–seem pathological, dysgenic, dead, the exception that must be quarantined to protect the health of the living.
Mackenzie uses credit reporting practices for trans people to study both the way big data practices oppress trans people specifically, and how trans people use glitches in the institutions that collect, aggregate, and report data to more easily navigate the world in their new identities.
Credit reports reduce identity to risk: they measure the risk creditors assume when investing in that person. For example, when cis women change their surname upon marriage, this is legible to credit reporting agencies as something that decreases risk (car insurance rates also lower when you get married–I have a funny story about this). But when a trans person changes their first name, this is “is illegible and suspect to credit reporting systems, even when it is legally sanctioned and documented” (46) because the name change is not useful to help protect others from making risky investments. In fact, keeping trans people from accessing past credit histories is useful to creditors, because being trans is risky: you can’t pay your rent or your credit card bill if you can’t find a job or get killed in a hate crime. Mackenzie notes that credit reports compound this riskiness: “consumer data sharing reproduces housing and employment insecurity for trans people through their credit reports” (46). Though they are certainly discriminated against for appearing perceptually trans (appearance, voice), through credit reports, trans people are also discriminated against for having risky credit histories.
Though they definitely punish trans people, credit reports can provide openings for trans people to hack otherwise oppressive institutions. Credit reporting is a massive bureaucratic enterprise. And, as every graduate student has figured out by now, big bureaucratic institutions (like banks or universites) fuck things up a lot–often the left hand doesn’t know that the right hand exists, let alone what it’s doing. As Mackenzie argues,
the materiality of the bureaucracy may work in the favor of illegible subjects at certain moments, disrupting the presumed omnipotent power of data systems to tell the truth of a person’s identity. Trans people take advantage of the confusing and chaotic processes of data identity production and push back against neoliberal capitalism, surveillance over their bodies, and the assertion that institutions must have the final word on which data are produced about them (58).
Bureaucracies have glitches. Though these are often a huge pain, for trans people they can be openings to hack otherwise seemingly omnipotent and impenetrable institutions.
One of Mackenzie’s most interesting points is that credit reports are about policing identity in order to ensure the legitimate (think Cooper here) transfer of property. As they argue,
credit reports aim to protect the transference of property to “proper” financial subjects…depending on which name(s) are present on a credit report, a trans person may apply for credit cards under two different names, be approved for one, both, or neither. This proliferation of identities is profitable for credit card companies so long as account holders remain responsible for paying back their debts (56).
Identity can be multiple as long as that facilitates the legitimate transfer of property from one respectable subject to another. Identity can be unstable and unfixed, as long as the property transfered to the person(s) with those identities is done so legitimately (in Cooper’s sense of legitimacy, this would mean transferred in a way that upholds white supremacist cisheteropatriarchal distributions of wealth, such as the distribution created by the Fordist family). In this way, Mackenzie’s argument extends Cooper’s analysis of the extension of credit to white gay men in the 1980s.
Finally, a question:
Mackenzie uses the metaphor of haunting to describe how data about one’s past behaviors relate to one’s present. For example, they argue:
Although describing an identity that may no longer exist (legally or otherwise), it lives on, tied to their other identifying characteristics: their Social Security number (SSN), last known addresses, debts…The very trouble that trans identities pose to administrative systems is this capacity for multiplicity that makes it possible for trans people (at least through their data) to exist in multiple forms in the past, present, and future. Trans people are indeed haunted by identification data in their credit reports; their previous names have an afterlife that extends indeterminately, sometimes long after a legal name change. (47).
This is the same metaphor that Stephen Dillon uses to describe slavery’s relationship to the Prison Industrial complex: in his article, Dillon argues that although slavery is mostly legally over and a thing of the past, people still perceive it and its effects in the present. Is Mackenzie pointing to a similar phenomenon? And if so, what is the broader role or significance of ‘haunting’ as a tool for social oppression? I’m thinking especially about haunting as a complementary technology to formal emancipation.