Digifesto

Tag: data science

December 7, 2015

Bourdieu and Horkheimer; towards an economy of control

It occurred to me as I looked over my earliest notes on Horkheimer (almost a year ago!) that Bourdieu’s concept of science as being a social field that formalizes and automates knowledge is Horkheimer’s idea of hell.

The danger Horkheimer (and so many others) saw in capitalist, instrumentalized, scientific society was that it would alienate and overwhelm the individual.

It is possible that society would alienate the individual anyway, though. For example, in the household of antiquity, were slaves unalienated? The privilege of autonomy is one that has always been rare but disproportionately articulated as normal, even a right. In a sense Western Democracies and Republics exist to guarantee autonomy to their citizens. In late modern democracies, autonomy is variable depending on role in society, which is tied to (economic, social, symbolic, etc.) capital.

So maybe the horror of Horkheimer, alienated by scientific advance, is the horror of one whose capital was being devalued by science. His scholarship, his erudition, were isolated and deemed irrelevant by the formal reasoners who had come to power.

As I write this, I am painfully aware that I have spent a lot of time in graduate school reading books and writing about them when I could have been practicing programming and learning more mathematics. My aspirations are to be a scientist, and I am well aware that that requires one to mathematically formalize ones findings–or, equivalently, to program them into a computer. (It goes without saying that computer programming is formalism, is automation, and so its central role in contemporary science or ‘data science’ is almost given to it by definition. It could not have been otherwise.)

Somehow I have been provoked into investing myself in a weaker form of capital, the benefit of which is the understanding that I write here, now.

Theoretically, the point of doing all this work is to be able to identify a societal value and formalize it so that it can be capture in a technical design. Perhaps autonomy is this value. Another might call it freedom. So once again I am reminded of Simone de Beauvoir’s philosophy of science, which has been correct all along.

But perhaps de Beauvoir was naive about the political implications of technology. Science discloses possibilities, the opportunities are distributed unequally because science is socially situated. Inequality leads to more alienation, not less, for all but the scientists. Meanwhile autonomy is not universally valued–some would prefer the comforts of society, of family structure. If free from society, they would choose to reenter it. Much of ones preferences must come from habitus, no?

I am indeed reaching the limits of my ability to consider the problem discursively. The field is too multidimensional, too dynamic. The proper next step is computer simulation.

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May 14, 2015

data science is not positivist, it’s power

Naively, we might assume that contemporary ‘data science’ is a form of positivist or post-positivist science. The scientist gathers data and subsumes it under logical formulae–models with fitted parameters. Indeed this is the case when data science is applied to natural phenomena, such as stars or the human genome.

The question of what kind of science ‘data science’ is becomes much more complex when we start to look at its application to social phenomena. This includes its application to the management of industrial and commercial technology–the so called “Internet of Things“. (Technology in general, and especially technology as situated socially, being a social phenomenon.)

There are (at least) two reasons why data science in these social domains is not strictly positivist.

The first is that, according to McKinsey’s Michael Chui, data science in the Internet of Things context is main about either real-time control or anomaly detection. Neither of these depends on the kind of nomothetic orientation that positivism requires. The former requires only an objective function over inputs to guide the steering of the dynamic system. The latter requires only the detection of deviation from historically observed patterns.

‘Data science’ applied in this context isn’t actually about the discovery of knowledge at all. It is not, strictly speaking, a science. Rather, it is a process through which the operations of existing technologies are related and improved by further technological interventions. Robust positivist engineering knowledge is applied to these cases. But however much the machines may ‘learn’, what they learn is not propositional.

Perhaps the best we can say is that ‘data science’ in this context is the science of techniques for making these kinds of interventions. As learning these techniques depends on mathematical rigor and empirical prototyping, we can say perhaps of the limited sense of ‘pure’ (not applied) data science that it is a positivist science.

But the second reason why data science is not positivist comes about as a result of its application. The problem is that when systems controlled by complex computational processes interact, the result is a more complex system. In adversarial cases, the interacting complex systems become the subject matter of cybersecurity research, towards which data science is one application. But as soon as on starts to study phenomena that are aware of the observer and can act in ways that respond to its presence, you get out of positivist territory.

A better way to think about data science might be to think of it in terms of perception. In, the visual system, data that comes in through the eye goes through many steps of preprocessing before it becomes the subject of attention. Visual representations feed into the control mechanisms of movement.

If we see data science not as a positivist attempt to discover natural laws, but rather as an extension of agency by expanding powers of perception and training skillful control, then we can get a picture of data science that’s consistent with theories of situated and embodied cognition.

These theories of situated and embodied cognition are perhaps the best contenders for what can displace the dominant paradigm as imagined by critics of cognitive science, economics, etc. Rather than being a rejection of explanatory power of naturalistic theories of information processing, these theories extend naive theories to embrace the complexity of how agents cognition is situated in a body in time, space, and society.

If we start to think of ‘data science’ not as a kind of natural science but as the techniques and tools for extending the information processing that is involved in ones individual or collective agency, then we can start to think about data science as what it really is: power.

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April 28, 2015

I really like Beniger

I’ve been a fan of Castells for some time but reading Ampuja and Koivisto’s critique of him is driving home my new appreciation of Beniger‘s The Control Revolution (1986).

One reason why I like Beniger is that his book is an account of social history and its relationship with technology that is firmly grounded in empirically and formally validated scientific theory. That is, rather than using as a baseline any political ideological framework, Beniger grounds his analysis in an understanding of the algorithm based in Church and Turing, and understanding of biological evolution grounded in biology, and so on.

This allows him to extend ideas about programming and control from DNA to culture to bureaucracy to computers in a way that is straightforward and plausible. His goal is, admirably, to get people to see the changes that technology drives in society as a continuation of a long regular process rather than a reason to be upset or a transformation to hype up.

I think there is something fundamentally correct about this approach. I mean that with the full force of the word correct. I want to go so far as to argue that Beniger (at least as of Chapter 3…) is an unideological theory of history and society that is grounded in generalizable and universally valid scientific theory.

I would be interested to read a substantive critique of Beniger arguing otherwise. Does anybody know if one exists?

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February 17, 2015

data science and the university

This is by now a familiar line of thought but it has just now struck me with clarity I wanted to jot down.

  1. Code is law, so the full weight of human inquiry should be brought to bear on software system design.
  2. (1) has been understood by “hackers” for years but has only recently been accepted by academics.
  3. (2) is due to disciplinary restrictions within the academy.
  4. (3) is due to the incentive structure of the academy.
  5. Since there are incentive structures for software development that are not available for subjects whose primary research project is writing, the institutional conditions that are best able to support software work and academic writing work are different.
  6. Software is a more precise and efficious way of communicating ideas than writing because its interpretation is guaranteed by programming language semantics.
  7. Because of (6), there is selective pressure to making software the lingua franca of scholarly work.
  8. (7) is inducing a cross-disciplinary paradigm shift in methods.
  9. (9) may induce a paradigm shift in theoretical content, or it may result in science whose contents are tailored to the efficient execution of adaptive systems. (This is not to say that such systems are necessarily atheoretic, just that they are subject to different epistemic considerations).
  10. Institutions are slow to change. That’s what makes them institutions.
  11. By (5), (7), and (9), the role of universities as the center of research is being threatened existentially.
  12. But by (1), the myriad intellectual threads currently housed in universities are necessary for software system design, or are at least potentially important.
  13. With (11) and (12), a priority is figuring out how to manage a transition to software-based scholarship without information loss.
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January 9, 2015

Horkheimer and Wiener

[I began writing this weeks ago and never finished it. I’m posting it here in its unfinished form just because.]

I think I may be condemning myself to irrelevance by reading so many books. But as I make an effort to read up on the foundational literature of today’s major intellectual traditions, I can’t help but be impressed by the richness of their insight. Something has been lost.

I’m currently reading Norbert Wiener’s The Human Use of Human Beings (1950) and Max Horkheimer’s Eclipse of Reason (1947). The former I am reading for the Berkeley School of Information Classics reading group. Norbert Wiener was one of the foundational mathematicians of 20th century information technology, a colleague of Claude Shannon. Out of his own sense of social responsibility, he articulated his predictions for the consequences of the technology he developed in Human Use. This work was the foundation of cybernetics, an influential school of thought in the 20th century. Terrell Bynum, in his Stanford Encyclopedia of Philosophy article on “Computer and Information Ethics“, attributes to Wiener’s cybernetics the foundation of all future computer ethics. (I think that the threads go back earlier, at least through to Heidegger’s Question Concerning Technology. (EDIT: Actually, QCT was published, it seems, in 1954, after Weiner’s book.)) It is hard to find a straight answer to the question of what happened to cybernetics?. By some reports, the artificial intelligence community cut their NSF funding in the 60’s.

Horkheimer is one of the major thinkers of the very influential Frankfurt School, the postwar social theorists at the core of intellectual critical theory. Of the Frankfurt School, perhaps the most famous in the United States is Adorno. Adorno is also the most caustic and depressed, and unfortunately much of popular critical theory now takes on his character. Horkheimer is more level-headed. Eclipse of Reason is an argument about the ways that philosophical empiricism and pragmatism became complicit in fascism. Here is an interested quotation.

It is very interesting to read them side by side. Published only a few years apart, Wiener and Horkheimer are giants of two very different intellectual traditions. There’s little reason to expect they ever communicated (a more thorough historian would know more). But each makes sweeping claims about society, language, and technology and contextualizes them in broader intellectual awareness of religion, history and science.

Horkheimer writes about how the collapse of the Enlightment project of objective reason has opened the way for a society ruled by subjective reason, which he characterizes as the reason of formal mathematics and scientific thinking that is neutral to its content. It is instrumental thinking in its purest, most rigorous form. His descriptions of it sound like gestures to what we today call “data science”–a set of mechanical techniques that we can use to analyze and classify anything, perfecting our understanding of technical probabilities towards whatever ends one likes.

I find this a more powerful critique of data science than recent paranoia about “algorithms”. It is frustrating to read something over sixty years old that covers the same ground as we are going over again today but with more composure. Mathematized reasoning about the world is an early 20th century phenomenon and automated computation a mid-20th century phenomenon. The disparities in power that result from the deployment of these tools were thoroughly discussed at the time.

But today, at least in my own intellectual climate, it’s common to hear a mention of “logic” with the rebuttal “whose logic?“. Multiculturalism and standpoint epistemology, profoundly important for sensitizing researchers to bias, are taken to an extreme the glorifies technical ignorance. If the foundation of knowledge is in one’s lived experience, as these ideologies purport, and one does not understand the technical logic used so effectively by dominant identity groups, then one can dismiss technical logic as merely a cultural logic of an opposing identity group. I experience the technically competent person as the Other and cannot perceive their actions as skill but only as power and in particular power over me. Because my lived experience is my surest guide, what I experience must be so!

It is simply tragic that the education system has promoted this kind of thinking so much that it pervades even mainstream journalism. This is tragic for reasons I’ve expressed in “objectivity is powerful“. One solution is to provide more accessible accounts of the lived experience of technicality through qualitative reporting, which I have attempted in “technical work“.

But the real problem is that the kind of formal logic that is at the foundation of modern scientific thought, including its most recent manifestation ‘data science’, is at its heart perfectly abstract and so cannot be captured by accounts of observed practices or lived experience. It is reason or thought. Is it disembodied? Not exactly. But at least according to constructivist accounts of mathematical knowledge, which occupy a fortunate dialectical position in this debate, mathematical insight is built from embodied phenomenological primitives but by their psychological construction are abstract. This process makes it possible for people to learn abstract principles such as the mathematical theory of information on which so much of the contemporary telecommunications and artificial intelligence apparatus depends. These are the abstract principles with which the mathematician Norbert Wiener was so intimately familiar.

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December 15, 2014

Some research questions

Last week was so interesting. Some weeks you just get exposed to so many different ideas that it’s trouble to integrate them. I tried to articulate what’s been coming up as a result. It’s several difficult questions.

  • Assuming trust is necessary for effective context management, how does one organize sociotechnical systems to provide social equity in a sustainable way?
  • Assuming an ecology of scientific practices, what are appropriate selection mechanisms (or criteria)? Are they transcendent or immanent?
  • Given the contradictory character of emotional reality, how can psychic integration occur without rendering one dead or at least very boring?
  • Are there limitations of the computational paradigm imposed by data science as an emerging pan-constructivist practice coextensive with the limits of cognitive or phenomenological primitives?

Some notes:

  • I think that two or three of these questions above may be in essence the same question. In that they can be formalized into the same mathematical problem, and the solution is the same in each case.
  • I really do have to read Isabelle Stengers and Nancy Nersessian. Based on the signals I’m getting, they seem to be the people most on top of their game in terms of understanding how science happens.
  • I’ve been assuming that trust relations are interpersonal but I suppose they can be interorganizational as well, or between a person and an organization. This gets back to a problem I struggle with in a recurring way: how do you account for causal relationships between a macro-organism (like an organization or company) and a micro-organism? I think it’s when there are entanglements between these kinds of entities that we are inclined to call something an “ecosystem”, though I learned recently that this use of the term bothers actual ecologists (no surprise there). The only things I know about ecology are from reading Ulanowicz papers, but those have been so on point and beautiful that I feel I can proceed with confidence anyway.
  • I don’t think there’s any way to get around having at least a psychological model to work with when looking at these sorts of things. A recurring an promising angle is that of psychic integration. Carl Jung, who has inspired clinical practices that I can personally vouch for, and Gregory Bateson both understood the goal of personal growth to be integration of disparate elements. I’ve learned recently from Turner’s The Democratic Surround that Bateson was a more significant historical figure than I thought, unless Turner’s account of history is a glorification of intellectuals that appeal to him, which is entirely possible. Perhaps more importantly to me, Bateson inspired Ulanowicz, and so these theories are compatible; Bateson was also a cyberneticist following Wiener, who was prescient and either foundational to contemporary data science or a good articulator of its roots. But there is also a tie-in to constructivist epistemology. DiSessa’s epistemology, building on Piaget but embracing what he calls the computational metaphor, understands the learning of math and physics as the integration of phenomenological primitives.
  • The purpose of all this is ultimately protocol design.
  • This does not pertain directly to my dissertation, though I think it’s useful orienting context.
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September 17, 2014

technical work

Dipping into Julian Orr’s Talking about Machines, an ethnography of Xerox photocopier technicians, has set off some light bulbs for me.

First, there’s Orr’s story: Orr dropped out of college and got drafted, then worked as a technician in the military before returning to school. He paid the bills doing technical repair work, and found it convenient to do his dissertation on those doing photocopy repair.

Orr’s story reminds me of my grandfather and great-uncle, both of whom were technicians–radio operators–during WWII. Their civilian careers were as carpenters, building houses.

My own dissertation research is motivated by my work background as an open source engineer, and my own desire to maintain and improve my technical chops. I’d like to learn to be a data scientist; I’m also studying data scientists at work.

Further fascinating was Orr’s discussion of the Xerox technician’s identity as technicians as opposed to customers:

The distinction between technician and customer is a critical division of this population, but for technicians at work, all nontechnicians are in some category of other, including the corporation that employs the technicians, which is seen as alien, distant, and only sometimes an ally.

It’s interesting to read about this distinction between technicians and others in the context of Xerox photocopiers when I’ve been so affected lately by the distinction between tech folk and others and data scientists and others. This distinction between those who do technical work and those who they serve is a deep historical one that transcends the contemporary and over-computed world.

I recall my earlier work experience. I was a decent engineer and engineering project manager. I was a horrible account manager. My customer service skills were abysmal, because I did not empathize with the client. The open source context contributes to this attitude, because it makes a different set of demands on its users than consumer technology does. One gets assistance with consumer grade technology by hiring a technician who treats you as a customer. You get assistance with open source technology by joining the community of practice as a technician. Commercial open source software, according to the Pentaho beekeeper model, is about providing, at cost, that customer support.

I’ve been thinking about customer service and reflecting on my failures at it a lot lately. It keeps coming up. Mary Gray’s piece, When Science, Customer Service, and Human Subjects Research Collide explicitly makes the connection between commercial data science at Facebook and customer service. The ugly dispute between Gratipay (formerly Gittip) and Shanley Kane was, I realized after the fact, a similar crisis between the expectations of customers/customer service people and the expectations of open source communities. When “free” (gratis) web services display a similar disregard for their users as open source communities do, it’s harder to justify in the same way that FOSS does. But there are similar tensions, perhaps. It’s hard for technicians to empathize with non-technicians about their technical problems, because their lived experience is so different.

It’s alarming how much is being hinged on the professional distinction between technical worker and non-technical worker. The intra-technology industry debates are thick with confusions along these lines. What about marketing people in the tech context? Sales? Are the “tech folks” responsible for distributional justice today? Are they in the throws of an ideology? I was reading a paper the other day suggesting that software engineers should be held ethically accountable for the implicit moral implications of their algorithms. Specifically the engineers; for some reason not the designers or product managers or corporate shareholders, who were not mentioned. An interesting proposal.

Meanwhile, at the D-Lab, where I work, I’m in the process of navigating my relationship between two teams, the Technical Team, and the Services Team. I have been on the Technical team in the past. Our work has been to stay on top of and assist people with data science software and infrastructure. Early on, we abolished regular meetings as a waste of time. Naturally, there was a suspicion expressed to me at one point that we were unaccountable and didn’t do as much work as others on the Services team, which dealt directly with the people-facing component of the lab–scheduling workshops, managing the undergraduate work-study staff. Sitting in on Services meetings for the first time this semester, I’ve been struck by how much work the other team does. By and large, it’s information work: calendering, scheduling, entering into spreadsheets, documenting processes in case of turnover, sending emails out, responding to emails. All important work.

This is exactly the work that information technicians want to automate away. If there is a way to reduce the amount of calendering and entering into spreadsheets, programmers will find a way. The whole purpose of computer science is to automate tasks that would otherwise be tedious.

Eric S. Raymond’s classic (2001) essay How to Become a Hacker characterizes the Hacker Attitude, in five points:

  1. The world is full of fascinating problems waiting to be solved.
  2. No problem should ever have to be solved twice.
  3. Boredom and drudgery are evil.
  4. Freedom is good.
  5. Attitude is no substitute for competence.

There is no better articulation of the “ideology” of “tech folks” than this, in my opinion, yet Raymond is not used much as a source for understanding the idiosyncracies of the technical industry today. Of course, not all “hackers” are well characterized by Raymond (I’m reminded of Coleman’s injunction to speak of “cultures of hacking”) and not all software engineers are hackers (I’m sure my sister, a software engineer, is not a hacker. For example, based on my conversations with her, it’s clear that she does not see all the unsolved problems with the world to be intrinsically fascinating. Rather, she finds problems that pertain to some human interest, like children’s education, to be most motivating. I have no doubt that she is a much better software engineer than I am–she has worked full time at it for many years and now works for a top tech company. As somebody closer to the Raymond Hacker ethic, I recognize that my own attitude is no substitute for that competence, and hold my sister’s abilities in very high esteem.)

As usual, I appear to have forgotten where I was going with this.

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September 16, 2014

frustrations with machine ethics

It’s perhaps because of the contemporary two cultures problem of tech and the humanities that machine ethics is in such a frustrating state.

Today I read danah boyd’s piece in The Message about technology as an arbiter of fairness. It’s more baffling conflation of data science with neoliberalism. This time, the assertion was that the ideology of the tech industry is neoliberalism hence their idea of ‘fairness’ is individualist and against social fabric. It’s not clear what backs up these kinds of assertions. They are more or less refuted by the fact that industrial data science is obsessed with our network of ties for marketing reasons. If anybody understands the failure of the myth of the atomistic individual, it’s “tech folks,” a category boyd uses to capture, I guess, everyone from marketing people at Google to venture capitalists to startup engineers to IBM researchers. You know, the homogenous category that is “tech folks.”

This kind of criticism makes the mistake of thinking that a historic past is the right way to understand a rapidly changing present that is often more technically sophisticated than the critics understand. But critical academics have fallen into the trap of critiquing neoliberalism over and over again. One problem is that tech folks don’t spend a ton of time articulating their ideology in ways that are convenient for pop culture critique. Often their business models require rather sophisticated understandings of the market, etc. that don’t fit readily into that kind of mold.

What’s needed is substantive progress in computational ethics. Ok, so algorithms are ethically and politically important. What politics would you like to see enacted, and how do you go about implementing that? How do you do it in a way that attracts new users and is competitively funded so that it can keep up with the changing technology with which we use to access the web? These are the real questions. There is so little effort spent trying to answer them. Instead there’s just an endless series of op-ed bemoaning the way things continue to be bad because it’s easier than having agency about making things better.

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August 27, 2014

a response to “Big Data and the ‘Physics’ of Social Harmony” by @doctaj; also Notes towards ‘Criticality as ideology’;

I’ve been thinking over Robin James’ “Big Data & the ‘Physics’ of Social Harmony“, an essay in three sections. The first discusses Singapore’s use of data science to detect terrorists and public health threats for the sake of “social harmony,” as reported by Harris in Foreign Policy. The second ties together Plato, Pentland’s “social physics”, and neoliberalism. The last discusses the limits to individual liberty proposed by J.S. Mill. The author admits it’s “all over the place.” I get the sense that it is a draft towards a greater argument. It is very thought-provoking and informative.

I take issue with a number of points in the essay. Underlying my disagreement is what I think is a political difference about the framing of “data science” and its impact on society. Since I am a data science practitioner who takes my work seriously, I would like this framing to be nuanced, recognizing both the harm and help that data science can do. I would like the debate about data science to be more concrete and pragmatic so that practitioners can use this discussion as a guide to do the right thing. I believe this will require discussion of data science in society to be informed by a technical understanding of what data science is up to. However, I think it’s also very important that these discussions rigorously take up the normative questions surrounding data sciences’ use. It’s with this agenda that I’m interested in James’ piece.

James is a professor of Philosophy and Women’s/Gender Studies and the essay bears the hallmarks of these disciplines. Situated in a Western and primarily anglophone intellectual tradition, it draws on Plato and Mill for its understanding of social harmony and liberalism. At the same time, it has the political orientation common to Gender Studies, alluding to the gendered division of economic labor, at times adopting Marxist terminology, and holding suspicion for authoritarian power. Plato is read as being the intellectual root of a “particular neoliberal kind of social harmony” that is “the ideal that informs data science.” James contrasts this ideal with the ideal of individual liberty, as espoused and then limited by Mill.

Where I take issue with James is that I think this line of argument is biased by its disciplinary formation. (Since this is more or less a truism for all academics, I suppose this is less a rebuttal than a critique.) Where I believe this is most visible is in her casting of Singapore’s ideal of social harmony as an upgrade of Plato, via the ideology of neoliberalism. She does not not consider in the essay that Singapore’s ideal of social harmony might be rooted in Eastern philosophy, not Western philosophy. Though I have no special access or insight into the political philosophy of Singapore, this seems to me to be an important omission given that Singapore is ethnically 74.2% Chinese and with Buddhist plurality.

Social harmony is a central concept in Eastern, especially Chinese, philosophy with deep roots in Confucianism and Daoism. A great introduction for those with background in Western philosophy who are interested in the philosophical contributions of Confucius is Fingarette’s Confucius: The Secular as Sacred. Fingarette discusses how Confucian thought is a reaction to the social upheaval and war of Anciant China’s Warring States Period, roughly 475 – 221 BC. Out of these troubling social conditions, Confucian thought attempts to establish conditions for peace. These include ritualized forms of social interaction at whose center is a benevolent Emperor.

There are many parallels with Plato’s political philosophy, but Fingarette makes a point of highlighting where Confucianism is different. In particular, the role of social ritual and ceremony as the basis of society is at odds with Western individualism. Political power is not a matter of contest of wills but the proper enactment of communal rites. It is like a dance. Frequently, the word “harmony” is used in the translation of Confucian texts to refer to the ideal of this functional, peaceful ceremonial society and, especially, its relationship with nature.

A thorough analysis of use of data science for social control in light of Eastern philosophy would be an important and interesting work. I certainly haven’t done it. My point is simply that when we consider the use of data science for social control as a global phenomenon, it is dubious to see it narrowly in light of Western intellectual history and ideology. That includes rooting it in Plato, contrasting it with Mill, and characterizing it primarily as an expression of white neoliberalism. Expansive use of these Western tropes is a projection, a fallacy of “I think this way, therefore the world must.” This I submit is an occupational hazard of anyone who sees their work primarily as an analysis of critique of ideology.

In a lecture in 1965 printed in Knowledge and Human Interests, Habermas states:

The concept of knowledge-constitutive human interests already conjoins the two elements whose relation still has to be explained: knowledge and interest. From everyday experience we know that ideas serve often enough to furnish our actions with justifying motives in place of the real ones. What is called rationalization at this level is called ideology at the level of collective action. In both cases the manifest content of statements is falsified by consciousness’ unreflected tie to interests, despite its illusion of autonomy. The discipline of trained thought thus correctly aims at excluding such interests. In all the sciences routines have been developed that guard against the subjectivity of opinion, and a new discipline, the sociology of knowledge, has emerged to counter the uncontrolled influence of interests on a deeper level, which derive less from the individual than from the objective situation of social groups.

Habermas goes on to reflect on the interests driving scientific inquiry–“scientific” in the broadest sense of having to do with knowledge. He delineates:

  • Technical inquiry motivated by the drive for manipulation and control, or power
  • Historical-hermeneutic inquiry motivated by the drive to guide collective action
  • Critical, reflexive inquiry into how the objective situation of social groups controls ideology, motivated by the drive to be free or liberated

This was written in 1965. Habermas was positioning himself as a critical thinker; however, unlike some of the earlier Frankfurt School thinkers he drew on, he did maintained that technical power was an objective human interest. (see Bohman and Rehg) In the United States especially, criticality as a mode of inquiry took aim at the ideologies that aimed at white, bourgeois, and male power. Contemporary academic critique has since solidified as an academic discipline and wields political power. In particular, is frequently enlisted as an expression of the interests of marginalized groups. In so doing, academic criticality has (in my view regrettably) becomes mere ideology. No longer interested in being scientifically disinterested, it has become a tool of rationalization. It’s project is the articulation of changing historical conditions in certain institutionally recognized tropes. One of these tropes is the critique of capitalism, modernism, neoliberalism, etc. and their white male bourgeois heritage. Another is the feminist emphasis on domesticity as a dismissed form on economic production. This trope features in James’ analysis of Singapore’s ideal of social harmony:

Harris emphasizes that Singaporeans generally think that finely-tuned social harmony is the one thing that keeps the tiny city-state from tumbling into chaos. [1] In a context where resources are extremely scarce–there’s very little land, and little to no domestic water, food, or energy sources, harmony is crucial. It’s what makes society sufficiently productive so that it can generate enough commercial and tax revenue to buy and import the things it can’t cultivate domestically (and by domestically, I really mean domestically, as in, by ‘housework’ or the un/low-waged labor traditionally done by women and slaves/servants.) Harmony is what makes commercial processes efficient enough to make up for what’s lost when you don’t have a ‘domestic’ supply chain. (emphasis mine)

To me, this parenthetical is quite odd. There are other uses of the word “domestic” that do not specifically carry the connotation of women and slave/servants. For example, the economic idea of gross domestic product just means “an aggregate measure of production equal to the sum of the gross values added of all resident institutional units engaged in production (plus any taxes, and minus any subsidies, on products not included in the value of their outputs).” Included in that production is work done by men and high-wage laborers. To suggest that natural resources are primarily exploited by “domestic” labor in the ‘housework’ sense is bizarre given, say, agribusiness, industrial mining, etc.

There is perhaps an interesting etymological relationship here; does our use of ‘domestic’ in ‘domestic product’ have its roots in household production? I wouldn’t know. Does that same etymological root apply in Singapore? Was agriculture in East Asia traditionally the province of household servants in China and Southeast Asia (as opposed to independent farmers and their sons?)? Regardless, domestic economic production agricultural production is not housework now. So it’s mysterious that this detail should play a role in explaining Singapore’s emphasis on social harmony today.

So I think it’s safe to say that this parenthetical remark by James is due to her disciplinary orientation and academic focus. Perhaps it is a contortion to satisfy the audience of Cyborgology, which has a critical left-leaning politics. A Harris’s original article does not appear to support this interpretation. Rather, it only uses the word ‘harmony’ twice, and maintains a cultural sensitivity that James’ piece lacks, noting that Singapore’s use of data science may be motivated by a cultural fear of loss or risk.

The colloquial word kiasu, which stems from a vernacular Chinese word that means “fear of losing,” is a shorthand by which natives concisely convey the sense of vulnerability that seems coded into their social DNA (as well as their anxiety about missing out — on the best schools, the best jobs, the best new consumer products). Singaporeans’ boundless ambition is matched only by their extreme aversion to risk.

If we think that Harris is closer to the source here, then we do not need the projections of Western philosophy and neoliberal theory to explain what is really meant by Singapore’s use of data science. Rather, we can look to Singapore’s culture and perhaps its ideological origins in East Asian thinking. Confucius, not Plato.

* * *

If there it is a disciplinary bias to American philosophy departments, it is that they exist to reproduce anglophone philosophy. This is point that James has recently expressed herself…in fact while I have been in the process of writing this response.

Though I don’t share James’ political project, generally speaking I agree that effort spent of the reproduction of disciplinary terminology is not helpful to the philosophical and scientific projects. Terminology should be deployed for pragmatic reasons in service to objective interests like power, understanding, and freedom. On the other hand, language requires consistency to be effective, and education requires language. My own personal conclusion on is that the scientific project can only be sustained now through disciplinary collapse.

When James suggests that old terms like metaphysics and epistemology prevent the de-centering of the “white supremacist/patriarchal/capitalist heart of philosophy”, she perhaps alludes to her recent coinage of “epistemontology” as a combination of epistemology and ontology, as a way of designating what neoliberalism is. She notes that she is trying to understand neoliberalism as an ideology, not as a historical period, and finds useful the definition that “neoliberals think everything in the universe works like a deregulated, competitive, financialized capitalist market.”

However helpful a philosophical understanding of neoliberalism as market epistemontology might be, I wonder whether James sees the tension between her statements about rejecting traditional terminology that reproduces the philosophical discipline and her interest in preserving the idea of “neoliberalism” in a way that can be be taught in an introduction to philosophy class, a point she makes in a blog comment later. It is, perhaps, in the act of teaching that a discipline is reproduced.

The use of neoliberalism as a target of leftist academic critique has been challenged relatively recently. Craig Hickman, in a blog post about Luis Suarez-Villa, writes:

In fact Williams and Srinicek see this already in their first statement in the interview where they remind us that “what is interesting is that the neoliberal hegemony remains relatively impervious to critique from the standpoint of the latter, whilst it appears fundamentally unable to counter a politics which would be able to combat it on the terrain of modernity, technology, creativity, and innovation.” That’s because the ball has moved and the neoliberalist target has shifted in the past few years. The Left is stuck in waging a war it cannot win. What I mean by that is that it is at war with a target (neoliberalism) that no longer exists except in the facades of spectacle and illusion promoted in the vast Industrial-Media-Complex. What is going on in the world is now shifting toward the East and in new visions of technocapitalism of which such initiatives as Smart Cities by both CISCO (see here) and IBM and a conglomerate of other subsidiary firms and networking partners to build new 21st Century infrastructures and architectures to promote creativity, innovation, ultra-modernity, and technocapitalism.

Let’s face it capitalism is once again reinventing itself in a new guise and all the Foundations, Think-Tanks, academic, and media blitz hype artists are slowly pushing toward a different order than the older market economy of neoliberalism. So it’s time the Left begin addressing the new target and its ideological shift rather than attacking the boogeyman of capitalism’s past. Oh, true, the façade of neoliberalism will remain in the EU and U.S.A. and much of the rest of the world for a long while yet, so there is a need to continue our watchdog efforts on that score. But what I’m getting at is that we need to move forward and overtake this new agenda that is slowly creeping into the mix before it suddenly displaces any forms of resistance. So far I’m not sure if this new technocapitalistic ideology has even registered on the major leftist critiques beyond a few individuals like Luis Suarez-Villa. Mark Bergfield has a good critique of Suarez-Villa’s first book on Marx & Philosophy site: here.

In other words, the continuation of capitalist domination is due to its evolution relative to the stagnation of intellectual critiques of it. Or to put it another way, privilege is the capacity to evolve and not merely reproduce. Indeed, the language game of academic criticality is won by those who develop and disseminate new tropes through which to represent the interests of the marginalized. These privileged academics accomplish what Lyotard describes as “legitimation through paralogy.”

* * * * *

If James were working merely within academic criticality, I would be less interested in the work. But her aspirations appear to be higher, in a new political philosophy that can provide normative guidance in a world where data science is a technical reality. She writes:

Mill has already made–in 1859 no less–the argument that rationalizes the sacrifice of individual liberty for social harmony: as long as such harmony is enforced as a matter of opinion rather than a matter of law, then nobody’s violating anybody’s individual rights or liberties. This is, however, a crap argument, one designed to limit the possibly revolutionary effects of actually granting individual liberty as more than a merely formal, procedural thing (emancipating people really, not just politically, to use Marx’s distinction). For example, a careful, critical reading of On Liberty shows that Mill’s argument only works if large groups of people–mainly Asians–don’t get individual liberty in the first place. [2] So, critiquing Mill’s argument may help us show why updated data-science versions of it are crap, too. (And, I don’t think the solution is to shore up individual liberty–cause remember, individual liberty is exclusionary to begin with–but to think of something that’s both better than the old ideas, and more suited to new material/technical realities.)

It’s because of these more universalist ambitions that I think it’s fair to point out the limits of her argument. If a government’s idea of “social harmony” is not in fact white capitalist but premodern Chinese, if “neoliberalism” is no longer the dominant ideology but rather an idea of an ideology reproduced by a stagnating academic discipline, then these ideas will not help us understand what is going on in the contemporary world in which ‘data science’ is allegedly of such importance.

What would be better than this?

There is an empirical reality to the practices of data science. Perhaps it should be studied on its own terms, without disciplinary baggage.

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