Digifesto

Tag: racial formation theory

Response to Abdurahman

Abdurahman has responded to my response to her tweet about my paper with Bruce Haynes, and invited me to write a rebuttal. While I’m happy to do so–arguing with intellectuals on the internet is probably one of my favorite things to do–it is not easy to rebut somebody with whom you have so little disagreement.

Abdurahman makes a number of points:

  1. Our paper, “Racial categories in machine learning”, omits the social context in which algorithms are enacted.
  2. The paper ignores whether computational thinking “acolytes like [me]” should be in the position of determining civic decisions.
  3. That the ontological contributions of African American Vernacular English (AAVE) are not present in the FAT* conference and that constitutes a hermeneutic injustice. (I may well have misstated this point).
  4. The positive reception to our paper may be due to its appeal to people with a disingenuous, lazy, or uncommitted racial politics.
  5. “Participatory design” does not capture Abdurahman’s challenge of “peer” design. She has a different and more broadly encompassing set of concerns: “whose language is used, whose viewpoint and values are privileged, whose agency is extended, and who has the right to frame the “problem”.”
  6. That our paper misses the point about predictive policing, from the perspective of people most affected by disparities in policing. Machine learning classification is not the right frame of the problem. The problem is an unjust prison system and, more broadly the unequal distribution of power that is manifested in the academic discourse itself. “[T]he problem is framed wrongly — it is not just that classification systems are inaccurate or biased, it is who has the power to classify, to determine the repercussions / policies associated thereof and their relation to historical and accumulated injustice?”

I have to say that I am not a stranger most of this line of thought and have great sympathy for the radical position expressed.

I will continue to defend our paper. Re: point 1, a major contribution of our paper was that it shed light on the political construction of race, especially race in the United States, which is absolutely part of “the social context in which algorithmic decision making is enacted”. Abdurahman must be referring to some other aspect of the social context. One problem we face as academic researchers is that the entire “social context” of algorithmic decision-making is the whole frickin’ world, and conference papers are about 12 pages or so. I thought we did a pretty good job of focusing on one, important and neglected aspect of that social context, the political formation of race, which as far as I know has never previously been addressed in a computer science paper. (I’ve written more about this point here).

Re: point 2, it’s true we omit a discussion of the relevance of computational thinking to civic decision-making. That is because this is a safe assumption to make in a publication to that venue. I happen to agree with that assumption, which is why I worked hard to submit a paper to that conference. If I didn’t think computational thinking was relevant, I probably would be doing something else with my time. That said, I think it’s wildly flattering and inaccurate to say that I, personally, have any control over “civic decision-making”. I really don’t, and I’m not sure why you’d think that, except for the erroneous myth that computer science research is, in itself, political power. It isn’t; that’s a lie that the tech companies have told the world.

I am quite aware (re: point 3) that my embodied and social “location” is quite different from Abdurahman’s. For example, unlike Abdurahman, it would be utterly pretentious for me to posture or “front” with AAVE. I simply have no access to its ontological wisdom, and could not be the conduit of it into any discourse, academic or informal. I have and use different resources; I am also limited by my positionality like anybody else. Sorry.

“Woke” white liberals potentially liking our argument? (Re: point 4) Fair. I don’t think that means our argument is bad or that the points aren’t worth making.

Re: point 5: I must be forgiven for not understanding the full depth of Abdurahman’s methodological commitments on the basis of a single tweet. There are a lot of different design methodologies and their boundaries are disputed. I see now that the label of “participatory design” is not sufficiently critical or radical enough to capture what she has in mind. I’m pleased to see she is working with Tap Parikh on this, who has a lot of experience with critical/radical HCI methods. I’m personally not an expert on any of this stuff. I do different work.

Re: point 6: My personal opinions about the criminal justice system did not make it into our paper, which again was a focused scientific article trying to make a different point. Our paper was about how racial categories are formed, how they are unfair, and how a computational system designed for fairness might address that problem. I agree that this approach is unlikely to have much meaningful impact on the injustices of the cradle-to-prison system in the United States, the prison-industrial complex, or the like. Based on what I’ve heard so far, the problems there would be best solved by changing the ways judges are trained. I don’t have any say in that, though–I don’t have a law degree.

In general, while I see Abdurahman’s frustrations as valid (of course!), I think it’s ironic and frustrating that she targets our paper as an emblem of the problems with the FAT* conference, with computer science, and with the world at large. First, our paper was not a “typical” FAT* paper; it was a very unusual one, positioned to broaden the scope of what’s discussed there, motivated in part by my own criticisms of the conference the year before. It was also just one paper: there’s tons of other good work at that conference, and the conversation is quite broad. I expect the best solution to the problem is to write and submit different papers. But it may also be that other venues are better for addressing the problems raised.

I’ll conclude that many of the difficulties and misunderstandings that underlie our conversation are a result of a disciplinary collapse that is happening because of academia’s relationship with social media. Language’s meaning depends on its social context, and social media is notoriously a place where contexts collapse. It is totally unreasonable to argue that everybody in the world should be focused on what you think is most important. In general, I think battles over “framing” on the Internet are stupid, and that the fact that these kinds of battles have become so politically prominent is a big part of why our society’s politics are so stupid. The current political emphasis on the symbolic sphere is a distraction from more consequential problems of economic and social structure.

As I’ve noted elsewhere, one reason why I think Haynes’s view of race is refreshing (as opposed to a lot of what passes for “critical race theory” in popular discussion) is that it locates the source of racial inequality in structure–spatial and social segregation–and institutional power–especially, the power of law. In my view, this politically substantive view of race is, if taken seriously, more radical than one based on mere “discourse” or “fairness” and demands a more thorough response. Codifying that response, in computational thinking, was the goal of our paper.

This is a more concrete and specific way of dealing with the power disparities that are at the heart of Abdurahman’s critique. Vague discourse and intimations about “privilege”, “agency”, and “power”, without an account of the specific mechanisms of that power, are weak.

All the problems with our paper, “Racial categories in machine learning”

Bruce Haynes and I were blown away by the reception to our paper, “Racial categories in machine learning“. This was a huge experiment in interdisciplinary collaboration for us. We are excited about the next steps in this line of research.

That includes engaging with criticism. One of our goals was to fuel a conversation in the research community about the operationalization of race. That isn’t a question that can be addressed by any one paper or team of researchers. So one thing we got out of the conference was great critical feedback on potential problems with the approach we proposed.

This post is an attempt to capture those critiques.

Need for participatory design

Khadijah Abdurahman, of Word to RI , issues a subtweeted challenge to us to present our paper to the hood. (RI stands for Roosevelt Island, in New York City, the location of the recently established Cornell Tech campus.)

One striking challenge, raised by Khadijah Abdurahman on Twitter, is that we should be developing peer relationships with the communities we research. I read this as a call for participatory design. It’s true this was not part of the process of the paper. In particular, Ms. Abdurahman points to a part of our abstract that uses jargon from computer science.

There are a lot of ways to respond to this comment. The first is to accept the challenge. I would personally love it if Bruce and I could present our research to folks on Roosevelt Island and get feedback from them.

There are other ways to respond that address the tensions of this comment. One is to point out that in addition to being an accomplished scholar of the sociology of race and how it forms, especially in urban settings, Bruce is a black man who is originally from Harlem. Indeed, Bruce’s family memoir shows his deep and well-researched familiarity with the life of marginalized people of the hood. So a “peer relationship” between an algorithm designer (me) and a member of an affected community (Bruce) is really part of the origin of our work.

Another is to point out that we did not research a particular community. Our paper was not human subjects research; it was about the racial categories that are maintained by the Federal U.S. government and which pervade society in a very general way. Indeed, everybody is affected by these categories. When I and others who looks like me are ascribed “white”, that is an example of these categories at work. Bruce and I were very aware of how different kinds of people at the conference responded to our work, and how it was an intervention in our own community, which is of course affected by these racial categories.

The last point is that computer science jargon is alienating to basically everybody who is not trained in computer science, whether they live in the hood or not. And the fact is we presented our work at a computer science venue. Personally, I’m in favor of universal education in computational statistics, but that is a tall order. If our work becomes successful, I could see it becoming part of, for example, a statistical demography curriculum that could be of popular interest. But this is early days.

The Quasi-Racial (QR) Categories are Not Interpretable

In our presentation, we introduced some terminology that did not make it into the paper. We named the vectors of segregation derived by our procedure “quasi-racial” (QR) vectors, to denote that we were trying to capture dimensions that were race-like, in that they captured the patterns of historic and ongoing racial injustice, without being the racial categories themselves, which we argued are inherently unfair categories of inequality.

First, we are not wedded to the name “quasi-racial” and are very open to different terminology if anybody has an idea for something better to call them.

More importantly, somebody pointed out that these QR vectors may not be interpretable. Given that the conference is not only about Fairness, but also Accountability and Transparency, this critique is certainly on point.

To be honest, I have not yet done the work of surveying the extensive literature on algorithm interpretability to get a nuanced response. I can give two informal responses. The first is that one assumption of our proposal is that there is something wrong with how race and racial categories are intuitive understood. Normal people’s understanding of race is, of course, ridden with stereotypes, implicit biases, false causal models, and so on. If we proposed an algorithm that was fully “interpretable” according to most people’s understanding of what race is, that algorithm would likely have racist or racially unequal outcomes. That’s precisely the problem that we are trying to get at with our work. In other words, when categories are inherently unfair, interpretability and fairness may be at odds.

The second response is that educating people about how the procedure works and why its motivated is part of what makes its outcomes interpretable. Teaching people about the history of racial categories, and how those categories are both the cause and effect of segregation in space and society, makes the algorithm interpretable. Teaching people about Principal Component Analysis, the algorithm we employ, is part of what makes the system interpretable. We are trying to drop knowledge; I don’t think we are offering any shortcuts.

Principal Component Analysis (PCA) may not be the right technique

An objection from the computer science end of the spectrum was that our proposed use of Principal Component Analysis (PCA) was not well-motivated enough. PCA is just one of many dimensionality reduction techniques–why did we choose it in particular? PCA has many assumptions about the input embedded within it, including the component vectors of interest are linear combinations of the inputs. What if the best QR representation is a non-linear combination of the input variables? And our use of unsupervised learning, as a general criticism, is perhaps lazy, since in order to validate its usefulness we will need to test it with labeled data anyway. We might be better off with a more carefully calibrated and better motivated alternative technique.

These are all fair criticisms. I am personally not satisfied with the technical component of the paper and presentation. I know the rigor of the analysis is not of the standard that would impress a machine learning scholar and can take full responsibility for that. I hope to do better in a future iteration of the work, and welcome any advice on how to do that from colleagues. I’d also be interested to see how more technically skilled computer scientists and formal modelers address the problem of unfair racial categories that we raised in the paper.

I see our main contribution as the raising of this problem of unfair categories, not our particular technical solution to it. As a potential solution, I hope that it’s better than nothing, a step in the right direction, and provocative. I subscribe to the belief that science is an iterative process and look forward to the next cycle of work.

Please feel free to reach out if you have a critique of our work that we’ve missed. We do appreciate all the feedback!

For fairness in machine learning, we need to consider the unfairness of racial categorization

Pre-prints of papers accepted to this coming 2019 Fairness, Accountability, and Transparency conference are floating around Twitter. From the looks of it, many of these papers add a wealth of historical and political context, which I feel is a big improvement.

A noteworthy paper, in this regard, is Hutchinson and Mitchell’s “50 Years of Test (Un)fairness: Lessons for Machine Learning”, which puts recent ‘fairness in machine learning’ work in the context of very analogous debates from the 60’s and 70’s that concerned the use of testing that could be biased due to cultural factors.

I like this paper a lot, in part because it is very thorough and in part because it tees up a line of argument that’s dear to me. Hutchinson and Mitchell raise the question of how to properly think about fairness in machine learning when the protected categories invoked by nondiscrimination law are themselves social constructs.

Some work on practically assessing fairness in ML has tackled the problem of using race as a construct. This echoes concerns in the testing literature that stem back to at least 1966: “one stumbles immediately over the scientific difficulty of establishing clear yardsticks by which people can be classified into convenient racial categories” [30]. Recent approaches have used Fitzpatrick skin type or unsupervised clustering to avoid racial categorizations [7, 55]. We note that the testing literature of the 1960s and 1970s frequently uses the phrase “cultural fairness” when referring to parity between blacks and whites.

They conclude that this is one of the areas where there can be a lot more useful work:

This short review of historical connections in fairness suggest several concrete steps forward for future research in ML fairness: Diving more deeply into the question of how subgroups are defined, suggested as early as 1966 [30], including questioning whether subgroups should be treated as discrete categories at all, and how intersectionality can be modeled. This might include, for example, how to quantify fairness along one dimension (e.g., age) conditioned on another dimension (e.g., skin tone), as recent work has begun to address [27, 39].

This is all very cool to read, because this is precisely the topic that Bruce Haynes and I address in our FAT* paper, “Racial categories in machine learning” (arXiv link). The problem we confront in this paper is that the racial categories we are used to using in the United States (White, Black, Asian) originate in the white supremacy that was enshrined into the Constitution when it was formed and perpetuated since then through the legal system (with some countervailing activity during the Civil Rights Movement, for example). This puts “fair machine learning” researchers in a bind: either they can use these categories, which have always been about perpetuating social inequality, or they can ignore the categories and reproduce the patterns of social inequality that prevail in fact because of the history of race.

In the paper, we propose a third option. First, rather than reify racial categories, we propose breaking race down into the kinds of personal features that get inscribed with racial meaning. Phenotype properties like skin type and ocular folds are one such set of features. Another set are events that indicate position in social class, such as being arrested or receiving welfare. Another set are facts about the national and geographic origin of ones ancestors. These facts about a person are clearly relevant to how racial distinctions are made, but are themselves more granular and multidimensional than race.

The next step is to detect race-like categories by looking at who is segregated from each other. We propose an unsupervised machine learning technique that works with the distribution of the phenotype, class, and ancestry features across spatial tracts (as in when considering where people physically live) or across a social network (as in when considering people’s professional networks, for example). Principal component analysis can identify what race-like dimensions capture the greatest amounts of spatial and social separation. We hypothesize that these dimensions will encode the ways racial categorization has shaped the social structure in tangible ways; these effects may include both politically recognized forms of discrimination as well as forms of discrimination that have not yet been surfaced. These dimensions can then be used to classify people in race-like ways as input to fairness interventions in machine learning.

A key part of our proposal is that race-like classification depends on the empirical distribution of persons in physical and social space, and so are not fixed. This operationalizes the way that race is socially and politically constructed without reifying the categories in terms that reproduce their white supremacist origins.

I’m quite stoked about this research, though obviously it raises a lot of serious challenges in terms of validation.

On “Racialization” (Omi and Winant, 2014)

Notes on Omi and Winant, 2014, Chapter 4, Section: “Racialization”.

Summary

Race is often seen as either an objective category, or an illusory one.

Viewed objectively, it is seen as a biological property, tied to phenotypic markers and possibly other genetic traits. It is viewed as an ‘essence’.
Omi and Winant argue that the concept of ‘mixed-race’ depends on this kind of essentialism, as it implies a kind of blending of essences. This is the view associated with “scientific” racism, most prevalent in the prewar era.

View as an illusion, race is seen as an ideological construct. An epiphenomenon of culture, class, or peoplehood. Formed as a kind of “false consciousness”, in the Marxist terminology. This view is associated with certain critics of affirmative action who argue that any racial classification is inherently racist.

Omi and Winant are critical of both perspectives, and argue for an understanding of race as socially real and grounded non-reducibly in phenomic markers but ultimately significant because of the social conflicts and interests constructed around those markers.

They define race as: “a concept that signifies and symbolizes signifiers and symbolizes social conflicts and interests by referring to different types of human bodies.”

The visual aspect of race is irreducible, and becomes significant when, for example, is becomes “understood as a manifestation of more profound differences that are situated within racially identified persons: intelligence, athletic ability, temperament, and sexuality, among other traits.” These “understandings”, which it must be said may be fallacious, “become the basis to justify or reinforce social differentiation.

This process of adding social significance to phenomic markers is, in O&W’s language, racialization, which they define as “the extension of racial meanings to a previously racially unclassified relationship, social practice, or group.” They argue that racialization happens at both macro and micro scales, ranging from the consolidation of the world-system through colonialization to incidents of racial profiling.

Race, then, is a concept that refer to different kinds of bodies by phenotype and the meanings and social practices ascribed to them. When racial concepts are circulated and accepted as ‘social reality’, racial difference is not dependent on visual difference alone, but take on a life of their own.

Omi and Winant therefore take a nuanced view of what it means for a category to be socially constructed, and it is a view that has concrete political implications. They consider the question, raised frequently, as to whether “we” can “get past” race, or go beyond it somehow. (Recall that this edition of the book was written during the Obama administration and is largely a critique of the idea, which seems silly now, that his election made the United States “post-racial”).

Omi and Winant see this framing as unrealistically utopian and based on extreme view that race is “illusory”. It poses race as a problem, a misconception of the past. A more effective position, they claim, would note that race is an element of social structure, not an irregularity in it. “We” cannot naively “get past it”, but also “we” do not need to accept the erroneous conclusion that race is a fixed biological given.

Comments

Omi and Winant’s argument here is mainly one about the ontology of social forms.
In my view, this question of social form ontology is one of the “hard problems”
remaining in philosophy, perhaps equivalent to if not more difficult than the hard problem of consciousness. So no wonder it is such a fraught issue.

The two poles of thinking about race that they present initially, the essentialist view and the epiphenomenal view, had their heyday in particular historical intellectual movements. Proponents of these positions are still popularly active today, though perhaps it’s fair to say that both extremes are now marginalized out of the intellectual mainstream. Despite nobody really understanding how social construction works, most educated people are probably willing to accept that race is socially constructed in one way or another.

It is striking, then, that Omi and Winant’s view of the mechanism of racialization, which involves the reading of ‘deeper meanings’ into phenomic traits, is essentially a throwback to the objective, essentializing viewpoint.
Perhaps there is a kind of cognitive bias, maybe representativeness bias or fundamental attribution bias, which is responsible for the cognitive errors that make racialization possible and persistent.

If so, then the social construction of race would be due as much to the limits of human cognition as to the circulation of concepts. That would explain the temptation to believe that we can ‘get past’ race, because we can always believe in the potential for a society in which people are smarter and are trained out of their basic biases. But Omi and Winant would argue that this is utopian. Perhaps the wisdom of sociology and social science in general is the conservative recognition of the widespread implications of human limitation. As the social expert, one can take the privileged position that notes that social structure is the result of pervasive cognitive error. That pervasive cognitive error is perhaps a more powerful force than the forces developing and propagating social expertise. Whether it is or is not may be the existential question for liberal democracy.

An unanswered question at this point is whether, if race were broadly understood as a function of social structure, it remains as forceful a structuring element as if it is understood as biological essentialism. It is certainly possible that, if understood as socially contingent, the structural power of race will steadily erode through such statistical processes as regression to the mean. In terms of physics, we can ask whether the current state of the human race(s) is at equilibrium, or heading towards an equilibrium, or diverging in a chaotic and path-dependent way. In any of these cases, there is possibly a role to be played by technical infrastructure. In other words, there are many very substantive and difficult social scientific questions at the root of the question of whether and how technical infrastructure plays a role in the social reproduction of race.

“The Theory of Racial Formation”: notes, part 1 (Cha. 4, Omi and Winant, 2014)

Chapter 4 of Omi and Winant (2014) is “The Theory of Racial Formation”. It is where they lay out their theory of race and its formation, synthesizing and improving on theories of race as ethnicity, race as class, and race as nation that they consider earlier in the book.

This rhetorical strategy of presenting the historical development of multiple threads of prior theory before synthesizing them into something new is familiar to me from my work with Helen Nissenbaum on Contextual Integrity. CI is a theory of privacy that advances prior legal and social theories by teasing out their tensions. This seems to be a good way to advance theory through scholarship. It is interesting that the same method of theory building can work in multiple fields. My sense is that what’s going on is that there is an underlying logic to this process which in a less Anglophone world we might call “dialectical”. But I digress.

I have not finished Chapter 4 yet but I wanted to sketch out the outline of it before going into detail. That’s because what Omi and Winant are presenting a way of understanding the mechanisms behind the reproduction of race that are not simplistically “systemic” but rather break it down into discrete operations. This is a helpful contribution; even if the theory is not entirely accurate, its very specificity elevates the discourse.

So, in brief notes:

For Omi and Winant, race is a way of “making up people”; they attribute this phrase to Ian Hacking but do not develop Hacking’s definition. Their reference to a philosopher of science does situate them in a scholarly sense; it is nice that they seem to acknowledge an implicit hierarchy of theory that places philosophy at the foundation. This is correct.

Race-making is a form of othering, of having a group of people identify another group as outsiders. Othering is a basic and perhaps unavoidable human psychological function; their reference for this is powell and Menendian (Apparently, john a. powell being one of these people like danah boyd who decapitalizes their name.)

Race is of course a social construct that is neither a fixed and coherent category nor something that is “unreal”. That is, presumably, why we need a whole book on the dynamic mechanisms that form it. One reason why race is such a dynamic concept is because (a) it is a way of organizing inequality in society, (b) the people on “top” of the hierarchy implied by racial categories enforce/reproduce that category “downwards”, (c) the people on the “bottom” of the hierarchy implied by racial categories also enforce/reproduce a variation of those categories “upwards” as a form of resistance, and so (d) the state of the racial categories at any particular time is a temporary consequence of conflicting “elite” and “street” variations of it.

This presumes that race is fundamentally about inequality. Omi and Winant believe it is. In fact, they think racial categories are a template for all other social categories that are about inequality. This is what they mean by their claim that race is a master category. It’s “a frame used for organizing all manner of political thought”, particularly political thought about liberation struggles.

I’m not convinced by this point. They develop it with a long discussion of intersectionality that is also unconvincing to me. Historically, they point out that sometimes women’s movements have allied with black power movements, and sometimes they haven’t. They want the reader to think this is interesting; as a data scientist, I see randomness and lack of correlation. They make the poignant and true point that “perhaps at the core of intersectionality practice, as well as theory, is the ‘mixed race’ category. Well, how does it come about that people can be ‘mixed’?” They then drop the point with no further discussion.

[Edit: While Omi and Winant do address the issue of what it means to be ‘mixed race’ in more depth later in the book, their treatment of intersectionality remains for me difficult. Race is a system of political categorization; however, racial categories are hereditary in a way that sexual categories are not. That is an important difference in how the categories are formed and maintained, one that is glossed over in O&W’s treatment of the subject, as well as in popular discourse.]

Omi and Winant make an intriguing comment, “In legal theory, the sexual contract and racial contract have often been compared”. I don’t know what this is about but I want to know more.

This is all a kind of preamble to their presentation of theory. They start to provide some definitions:

racial formation
The sociohistorical process by which racial identities are created, lived out, transformed, and destroyed.
racialization
How phenomic-corporeal dimensions of bodies acquire meaning in social life.
racial projects
The co-constitutive ways that racial meanings are translated into social structures and become racially signified.
racism
Not defined. A property of racial projects that Omi and Winant will discuss later.
racial politics
Ways that the politics (of a state?) can handle race, including racial despotism, racial democracy, and racial hegemony.

This is a useful breakdown. More detail in the next post.

Notes on Omi and Winant, 2014, “Ethnicity”

I’m continuing to read Omi and Winant’s Racial Formation in the United States (2014). These are my notes on Chapter 1, “Ethnicity”.

There’s a long period during which the primary theory of race in the United States is a theological and/or “scientific” racism that maintains that different races are biologically different subspecies of humanity because some of them are the cursed descendants of some tribe mentioned in the Old Testament somewhere. In the 1800’s, there was a lot of pseudoscience involving skull measurements trying to back up a biblical literalism that rationalized, e.g., slavery. It was terrible.

Darwinism and improved statistical methods started changing all that, though these theological/”scientific” ideas about race were prominent in the United States until World War II. What took them out of the mainstream was the fact that the Nazis used biological racism to rationalize their evilness, and the U.S. fought them in a war. Jewish intellectuals in the United States in particular (and by now there were a lot of them) forcefully advocated for a different understanding of race based on ethnicity. This theory was dominant as a replacement for theories of scientific racism between WWII and the mid-60’s, when it lost its proponents on the left and morphed into a conservative ideology.

To understand why this happened, it’s important to point out how demographics were changing in the U.S. in the 20th century. The dominant group in the United States in the 1800’s were White Anglo-Saxon Protestants, or WASPs. Around 1870-1920, the U.S. started to get a lot more immigrants from Southern and Eastern Europe, as well as Ireland. These often economic refugees, though there were also people escaping religious persecution (Jews). Generally speaking these immigrants were not super welcome in the United States, but they came in at what may be thought of as a good time, as there was a lot of economic growth and opportunity for upward mobility in the coming century.

Partly because of this new wave of immigration, there was a lot of interest in different ethnic groups and whether or not they would assimilate in with the mainstream Anglo culture. American pragmatism, of the William James and Jown Dewey type, was an influential philosophical position in this whole scene. The early ethnicity theorists, who were part of the Chicago school of sociology that was pioneering grounded, qualitative sociological methods, were all pragmatists. Robert Park is a big figure here. All these guys apparently ripped off W.E.B. Du Bois, who was trained by William James and didn’t get enough credit because he was black.

Based on the observation of these European immigrants, the ethnicity theorists came to the conclusion that if you lower the structural barriers to participation in the economy, “ethnics” will assimilate to the mainstream culture (melt into the “melting pot”) and everything is fine. You can even tolerate some minor ethnic differences, resulting in the Italian-Americans, the Irish-Americans, and… the African-American. But that was a bigger leap for people.

What happened, as I’ve mentioned, is that scientific racism was discredited in the U.S. partly because it had to fight the Nazis and had so many Jewish intellectuals, who had been on the wrong end of scientific racism in Europe and who in the U.S. were eager to become “ethnics”. These became, in essence, the first “racial liberals”. At the time there was also a lot of displacement of African Americans who were migrating around the U.S. in search of economic opportunities. So in the post-war period ethnicity theorists optimistically proposed that race problems could be solved by treating all minority groups as if they were Southern and Eastern European immigrant groups. Reduce enough barriers and they would assimilate and/or exist in a comfortable equitable pluralism, they thought.

The radicalism of the Civil Rights movement broke the spell here, as racial minorities began to demand not just the kinds of liberties that European ethnics had taken advantage of, but also other changes to institutional racism and corrections to other racial injustices. The injustices persisted in part because racial differences are embodied differently than ethnic differences. This is an academic way of saying that the fact that (for example) black people often look different from white people matters for how society treats them. So treating race as a matter of voluntary cultural affiliation misses the point.

So ethnicity theory, which had been critical for dismantling scientific racism and opening the door for new policies on race, was ultimately rejected by the left. It was picked up by neoconservatives through their policies of “colorblindness”, which Omi and Winant describe in detail in the latter parts of their book.

There is a lot more detail in the chapter, which I found quite enlightening.

My main takeaways:

  • In today’s pitched media battles between “Enlightenment classical liberalism” and “postmodern identity politics”, we totally forget that a lot of American policy is based on American pragmatism, which is definitely neither an Enlightenment position nor postmodern. Everybody should shut up and read The Metaphysical Club.
  • There has been a social center, with views that are seen as center-left or center-right depending on the political winds, since WWII. The adoption of ethnicity theory into the center was a significant culture accomplishment with a specific history, however ultimately disappointing its legacy has been for anti-racist activists. Any resurgence of scientific racism is a definite backslide.
  • Omi and Winant are convincing about the limits of ethnicity theory in terms of: its dependence on economic “engines of mobility” that allow minorities to take part in economic growth, its failure to recognize the corporeal and ocular aspects of race, and its assumption that assimilation is going to be as appealing to minorities as it is to the white majority.
  • Their arguments about colorblind racism, which are at the end of their book, are going to be doing a lot of work and the value of the new edition of their book, for me at least, really depends on the strength of that theory.

Notes on Racial Formation by Omi and Winant, 2014, Introduction

Beginning to read Omi and Winant, Racial Formation in the United States, Third Edition, 2014. These are notes on the introduction, which outlines the trajectory of their book. This introduction is available on Google Books.

Omi and Winant are sociologists of race and their aim is to provide a coherent theory of race and racism, particularly as a United States phenomenon, and then to tell a history of race in the United States. One of their contentions is that race is a social construct and therefore varies over time. This means, in principle, that racial categories are actionable, and much of their analysis is about how anti-racist and racial reaction movements have transformed the politics and construction of race over the course of U.S. history. On the other hand, much of their work points to the persistence of racial categories despite the categorical changes.

Since the Third Edition, in 2014, comes twenty years after the Second Edition, much of the new material in the book addresses specifically what they call colorblind racial hegemony. This is a response to the commentary and question around the significance of Barack Obama’s presidency for race in America. It is interesting reading this in 2018, as in just a few brief years it seems like things have changed significantly. It’s a nice test, then to ask to what extent their theory explains what happened next.

Here is, broadly speaking, what is going on in their book based on the introduction.

First, they discuss prior theories of race they can find in earlier scholarship. They acknowledge that these are interesting lenses but believe they are ultimately reductionist. They will advance their own theory of racial formation in contrast with these. In the background of this section but dismissed outright is the “scientific” racism and religious theories of race that were prevalent before World War II and were used to legitimize what Omi and Winant call racial domination (this has specific meaning for them). Alternative theories of race that Omi and Winant appear to see as constructive contributions to racial theory include:

  • Race as ethnicity. As an alternative to scientific racism, post WWII thinkers advanced the idea of racial categories as reducing to ethnic categories, which were more granular social units based on shared and to some extent voluntary culture. This conception of race could be used for conflicting political agendas, including both pluralism and assimilation.
  • Race as class. The theory attempted to us economic theories–including both Marxist and market-based analysis–to explain race. Omi and Winant think this–especially the Marxist theory–was a productive lens but ultimate a reductive one. Race cannot be subsumed to class.
  • Race as nationality. Race has been used as the basis for national projects, and is tied up with the idea of “peoplehood”. In colonial projects especially, race and nationality are used both to motivate subjugation of a foreign people, and is also used in resistance movements to resist conquest.

It is interesting that these theories of race are ambiguous in their political import. Omi and Winant do a good job of showing how multi-dimensional race really is. Ultimately they reject all these theories and propose their own, racial formation theory. I have not read their chapter on it yet, so all I know is that: (a) they don’t shy away from the elephant in the room, which is that there is a distinctively ‘ocular’ component to race–people look different from each other in ways that are hereditary and have been used for political purposes, (b) they maintain that despite this biological aspect of race, the social phenomenon of race is a social construct and primarily one of political projects and interpretations, and (c) race is formed by a combination of action both at the representational level (depicting people in one way or another) and at the institutional level, with the latter determining real resource allocation and the former providing a rationalization for it.

Complete grokking of the racial formation picture is difficult, perhaps. This may be why instead of having a mainstream understanding of racial formation theory, we get reductive and ideological concepts of race active in politics. The latter part of Omi and Winant’s book is their historical account of the “trajectory” of racial politics in the United States, which they see in terms of a pendulum between anti-racist action (with feminist, etc., allies) and “racial reaction”–right-wing movements that subvert the ideas used by the anti-racists and spin them around into a backlash.

Omi and Winant describe three stages of racial politics in United States history:

  • Racial domination. Slavery and Jim Crow before WWII, based on religious and (now discredited, pseudo-)scientific theories of racial difference.
  • Racial hegemony. (Nod to Gramsci) Post-WWII race relations as theories of race-as-ethnicity open up egalitarian ideals. Opens way for Civil Rights movement.
  • Colorblind racism. A phase where the official ideology denies the significance of race in society while institutions continue to reinforce racial differences in a pernicious way. Necessarily tied up with neoliberalism, in Omi and Winant’s view.

The question of why colorblind racism is a form of racism is a subtle one. Omi and Winant do address this question head on, and I am in particular looking forward to their articulation of the point. Their analysis was done during the Obama presidency, which did seem to move the needle on race in a way that we are still seeing the repercussions of today. I’m interested in comparing their analysis with that of Fraser and Gilman. There seem to be some productive alignments and tensions there.

population traits, culture traits, and racial projects: a methods challenge #ica18

In a recent paper I’ve been working on with Mark Hannah that he’s presenting this week at the International Communications Association conference, we take on the question of whether and how “big data” can be used to study the culture of a population.

By “big data” we meant, roughly large social media data sets. The pitfalls of using this sort of data for any general study of a population are perhaps best articled by Tufekci (2014). In short: studies based on social media data are often sampling on the dependent variable because they only consider the people representing themselves on social media, though this is only a small portion of the population. To put it another way, the sample suffers from the 1% rule of Internet cultures: for any on-line community, only 1% create content, 10% interact with the content somehow, and the rest lurk. The behavior and attitudes of the lurkers, in addition to any field effects in the “background” of the data (latent variables in the social field of production), are all out of band and so opaque to the analyst.

By “the culture of a population”, we meant something specific: the distribution of values, beliefs, dispositions, and tastes of a particular group of people. The best source we found on this was Marsden and Swingle (1994), and article from a time before the Internet had started to transform academia. Then and perhaps now, the best way to study the distribution of culture across a broad population was a survey. The idea is that you sample the population according to some responsible statistics, you ask them some questions about their values, beliefs, dispositions, and tastes, and you report the results. Viola!

(Given the methodological divergence here, the fact that many people, especially ‘people on the Internet’, now view culture mainly through the lens of other people on the Internet is obviously a huge problem. Most people are not in this sample, and yet we pretend that it is representative because it’s easily available for analysis. Hence, our concept of culture (or cultures) is screwy, reflecting much more than is warranted whatever sorts of cultures are flourishing in a pseudonymous, bot-ridden, commercial attention economy.)

Can we productively combine social media data with surveys methods to get a better method for studying the culture of a population? We think so. We propose the following as a general method framework:

(1) Figure out the population of interest by their stable, independent ‘population traits’ and look for their activity on social media. Sample from this.

(2) Do exploratory data analysis to inductively get content themes and observations about social structure from this data.

(3) Use the inductively generated themes from step (2) to design a survey addressing cultural traits of the population (beliefs, values, dispositions, tastes).

(4) Conduct a stratified sample specifically across social media creators, synthesizers (e.g. people who like, retweet, and respond), and the general population and/or known audience, and distribute the survey.

(5) Extrapolate the results to general conclusions.

(6) Validate the conclusions with other data or not discrepancies for future iterations.

I feel pretty good about this framework as a step forward, except that in the interest of time we had to sidestep what is maybe the most interesting question raised by it, which is: what’s the difference between a population trait and a cultural trait.

Here’s what we were thinking:

Population trait Cultural trait
Location Twitter use (creator, synthesizer, lurker, none)
Age Political views: left, right, center
Permanent unique identifier Attitude towards media
Preferred news source
Pepsi or coke?

One thing to note: we decided that traits about media production and consumption were a subtype of cultural traits. I.e., if you use Twitter, that’s a particular cultural trait that may be correlated with other cultural traits. That makes the problem of sampling on the dependent variable explicit.

But the other thing to note is that there are certain categories that we did not put on this list. Which ones? Gender, race, etc. Why not? Because choosing whether these are population traits or cultural traits opens a big bag of worms that is the subject of active political contest. That discussion was well beyond the scope of the paper!

The dicey thing about this kind of research is that we explicitly designed it to try to avoid investigator bias. That includes the bias of seeing the world through social categories that we might otherwise naturalize of reify. Naturally, though, if we were to actually conduct this method on a sample, such as, I dunno, a sample of Twitter-using academics, we would very quickly discover that certain social categories (men, women, person of color, etc.) were themes people talked about and so would be included as survey items under cultural traits.

That is not terrible. It’s probably safer to do that than to treat them like immutable, independent properties of a person. It does seem to leave something out though. For example, say one were to identify race as a cultural trait and then ask people to identify with a race. Then one takes the results, does a factor analysis, and discovers a factor that combines a racial affinity with media preferences and participation rates. It then identifies the prevalence of this factor in a certain region with a certain age demographic. One might object to this result as a representation of a racial category as entailing certain cultural categories, and leaving out the cultural minority within a racial demographic that wants more representation.

This is upsetting to some people when, for example, Facebook does this and allows advertisers to target things based on “ethnic affinity”. Presumably, Facebook is doing just this kind of factor analysis when they identify these categories.

Arguably, that’s not what this sort of science is for. But the fact that the objection seems pertinent is an informative intuition in its own right.

Maybe the right framework for understanding why this is problematic is Omi and Winant’s racial formation theory (2014). I’m just getting into this theory recently, at the recommendation of Bruce Haynes, who I look up to as an authority on race in America. According to racial projects theory, racial categories are stable because they include both representations of groups of people as having certain qualities and social structures controlling the distribution of resources. So, the white/black divide in the U.S. is both racial stereotypes and segregating urban policy, because the divide is stable because of how the material and cultural factors reinforce each other.

This view is enlightening because it helps explain why hereditary phenotype, representations of people based on hereditary phenotype, requests for people to identify with a race even when this may not make any sense, policies about inheritance and schooling, etc. all are part of the same complex. When we were setting out to develop the method described above, we were trying to correct for a sampling bias in media while testing for the distribution of culture across some objectively determinable population variables. But the objective qualities (such as zip code) are themselves functions of the cultural traits when considered over the course of time. In short, our model, which just tabulates individual differences without looking at temporal mechanisms, is naive.

But it’s a start, if only to an interesting discussion.

References

Marsden, Peter V., and Joseph F. Swingle. “Conceptualizing and measuring culture in surveys: Values, strategies, and symbols.” Poetics 22.4 (1994): 269-289.

Omi, Michael, and Howard Winant. Racial formation in the United States. Routledge, 2014.

Tufekci, Zeynep. “Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls.” ICWSM 14 (2014): 505-514.