Digifesto

Category: academia

statistics, values, and norms

Further considering the difficulties of doing an ethnography of data science, I am reminded of Hannah Arendt’s comments about the apolitically of science.

The problem is this:

  • Ethnography as a practice is, as far as I can tell, descriptive. It is characterized primarily by non-judgmental observation.
  • Data scientific practice is tied to an epistemology of statistics. Statistics is a discipline about values and norms for belief formation. While superficially it may appear to have no normative content, practicing statistical thinking in research is a matter of adherence to norms.
  • It is very difficult to reconcile ethnographic epistemology and statistical epistemology. They have completely different intellectual histories and are based in very different cognitive modalities.
  • Ethnographers are often trained to reject statistical epistemology in their own work and as a result don’t learn statistics.
  • Consequently, most ethnographies of data science practice will leave out precisely that which data scientists see as most essential to their practice.

“Statistics” here is not entirely accurate. In computational statistics or ‘data science’, we can replace “statistics” with a large body of knowledge spanning statistics, probability theory, theory of computation, etc. The hybridization of these bodies of knowledge in, for example, educational curricula, is an interesting shift in the trajectory of science as a whole.

A deeper concern: in the self-understanding of the sciences, there is a transmitted sense of this intellectual history. In many good textbooks on technical subject-matter, there is a section at the end of each chapter on the history of the field. I come to these sections of the textbook with a sense of reverence. They stand as testaments to the century of cumulative labor done by experts on which our current work stands.

When this material is of disinterest to the historian or ethnographer of science, it feels like a kind of sacrilege. Contrast this disinterest with the treatment of somebody like Imre Lakatos, whose history of mathematics is so much more a history of the mathematics, not a history of the mathematicians, that the form of the book is a dialog compressing hundreds of years of mathematical history into a single classroom discussion. Historical detail is provided in footnotes, apart from the main drama of the narrative–which is about the emergence of norms of reasoning over time.

Observations of ethnographers

This semester the Berkeley Institute of Data Science Ethnography and Evaluation Working Group (EEWG) is meeting in its official capacity for the first time. In this context I am a data scientist among ethnographers and the transition to participation in this strange, alien culture is not an easy one. I have prepared myself for this task through coursework and associations throughout my time at Berkeley, but I am afraid that integrating into an “Science and Technology Studies” ethnographic team will be difficult nonetheless.

Off the bat, certain cultural differences seem especially salient:

In the sciences, typically one cares about whether or not the results of your investigation are true or useful in an intersubjective sense. This sense of purpose leads to a sense of concern for the logical coherence and rigor of ones method, which in turn constrains the kinds of questions that can be asked and the ways in which one presents ones results. Within STS, methodological concerns are perhaps secondary. The STS ethnographer interviews people, reads and listens carefully, but also holds the data at a distance. Ultimately, the process of writing–which is necessarily tied up with what the writer is interested in–is as much a part of the method as the observations and the analysis. Whereas the scientist strives for intersubjective agreement, the ethnographer is methodologically bound to their own subjectivity.

A consequence of this is that agonism, or the role of argumentation and disagreement, is different within scientific and ethnographic communities. (I owe this point to my colleague, Stuart Geiger, an ethnographer who is also in the working group.) In a scientific community argument is seen as a necessary step towards resolving disagreement and arriving at intersubjective results. The purpose of argument is, ideally, to arrive at agreement. Reasons are shared and disagreements resolved through logic. In the ethnographic community, since intersubjectivity is not valued, rational argument is seen more as form of political or social maneuvering. To challenge somebody intellectually is not simply to challenge what they are intellectualizing; it is to challenge their intellectual authority or competence.

This raises an interesting question: what is competence, to ethnographers? To the scientist, competence is a matter of concrete skills (handling lab equipment, computation, reasoning, presentation of results, etc.) that facilitate the purpose of science, the achievement of intellectual agreement on matters within the domain. Somebody who succeeds by virtue of skills other than these (such as political skilfulness) is seen, by the scientist, as a charlatan and a danger to the scientific project. Many of the more antisocial tendencies of scientists can be understood as an effort to keep the charlatans out, in order to preserve the integrity (and, ultimately, authority) of the scientific project.

Ethnographic competence is mysterious to me because, at least in STS, scientific authority is always a descriptively assessed social phenomenon and not something which one trusts. If the STS ethnographer sees the scientific project primarily as one of cultural and institutional regularity and leaves out the teleological aspects of science as a search for truth of some kind, as has been common in STS since the 80’s, then how can STS see its own intellectual authority besides as a rather arbitrary political accomplishment? What is competence besides the judgement of competence by ones bureaucratic superiors?

I am not sure that these questions, which seem pertinent to me as a scientist, are even askable within the language and culture of STS. As they concern the normative elements of intellectual inquiry, not descriptions of the social institutions of inquiry, they are in a sense “unscientific” questions vis-a-vis STS.

Perhaps more importantly, these questions are unimportant to the STS ethnographer because they are not relevant to the STS ethnographer’s job. In this way the STS ethnographer is not unlike many practicing scientists who, once they learn an approved method and have a community in which to share their work, do not question the foundations of their field. And perhaps because of STS’s concern with what others might consider the mundane aspects of scientific inquiry–the scheduling of meetings, the arrangement of events, the circulation of invitations and rejection letters, the arrangement of institutions–their concept of intellectual work hinges on these activities more than it does argument or analysis, relative to the sciences.

Nissenbaum the functionalist

Today in Classics we discussed Helen Nissenbaum’s Privacy in Context.

Most striking to me is that Nissenbaum’s privacy framework, contextual integrity theory, depends critically on a functionalist sociological view. A context is defined by its information norms and violations of those norms are judged according to their (non)accordance with the purposes and values of the context. So, for example, the purposes of an educational institution determine what are appropriate information norms within it, and what departures from those norms constitute privacy violations.

I used to think teleology was dead in the sciences. But recently I learned that it is commonplace in biology and popular in ecology. Today I learned that what amounts to a State Philosopher in the U.S. (Nissenbaum’s framework has been more or less adopted by the FTC) maintains a teleological view of social institutions. Fascinating! Even more fascinating that this philosophy corresponds well enough to American law as to be informative of it.

From a “pure” philosophy perspective (which is I will admit simply a vice of mine), it’s interesting to contrast Nissenbaum with…oh, Horkheimer again. Nissenbaum sees ethical behavior (around privacy at least) as being behavior that is in accord with the purpose of ones context. Morality is given by the system. For Horkheimer, the problem is that the system’s purposes subsume the interests of the individual, who is alone the agent who is able to determine what is right and wrong. Horkheimer is a founder of a Frankfurt school, arguably the intellectual ancestor of progressivism. Nissenbaum grounds her work in Burke and her theory is admittedly conservative. Privacy is violated when people’s expectations of privacy are violated–this is coming from U.S. law–and that means people’s contextual expectations carry more weight than an individual’s free-minded beliefs.

The tension could be resolved when free individuals determine the purpose of the systems they participate in. Indeed, Nissenbaum quotes Burke in his approval of established conventions as being the result of accreted wisdom and rationale of past generations. The system is the way it is because it was chosen. (Or, perhaps, because it survived.)

Since Horkheimer’s objection to “the system” is that he believes instrumentality has run amok, thereby causing the system serve a purpose nobody intended for it, his view is not inconsistent with Nissenbaum’s. Nissenbaum, building on Dworkin, sees contextual legitimacy as depending on some kind of political legitimacy.

The crux of the problem is the question of what information norms comprise the context in which political legitimacy is formed, and what purpose does this context or system serve?

And now for something completely different: Superintelligence and the social sciences

This semester I’ll be co-organizing, with Mahendra Prasad, a seminar on the subject of “Superintelligence and the Social Sciences”.

How I managed to find myself in this role is a bit of a long story. But as I’ve had a longstanding curiosity about this topic, I am glad to be putting energy into the seminar. It’s a great opportunity to get exposure to some of the very interesting work done by MIRI on this subject. It’s also a chance to thoroughly investigate (and critique) Bostrom’s book Superintelligence: Paths, Dangers, and Strategies.

I find the subject matter perplexing because in many ways it forces the very cultural and intellectual clash that I’ve been preoccupied with elsewhere on this blog: the failure of social scientists and engineers to communicate. Or, perhaps, the failure of qualitative researchers and quantitative researchers to communicate. Whatever you want to call it.

Broadly, the question at stake is: what impact will artificial intelligence have on society? This question is already misleading since in the imagination of most people who haven’t been trained in the subject, “artificial intelligence” refers to something of a science fiction scenario, whereas to practitioner, “artificial intelligence” is, basically, just software. Just as the press went wild last year speculating about “algorithms”, by which it meant software, so too is the press excited about artificial intelligence, which is just software.

But the concern that software is responsible for more and more of the activity in the world and that it is in a sense “smarter than us”, and especially the fear that it might become vastly smarter than us (i.e. turning into what Bostrom calls a “superintelligence”), is pervasive enough to drive research funding into topics like “AI Safety”. It also is apparently inspiring legal study into the regulation of autonomous systems. It may also have implications for what is called, vaguely, “social science”, though increasingly it seems like nobody really knows what that is.

There is a serious epistemological problem here. Some researchers are trying to predict or forewarn the societal impact of agents that are by assumption beyond their comprehension on the premise that they may come into existence at any moment.

This is fascinating but one has to get a grip.

One Magisterium: a review (part 1)

I have come upon a remarkable book, titled One Magisterium: How Nature Knows Through Us, by Seán Ó Nualláin, President, University of Ireland, California. It is dedicated “To all working at the edges of society in an uncompromising search for truth and justice.” It’s acknowledgement section opens:

Kenyan middle-distance runners were famous for running like “scared rabbits”: going straight to the head of the field and staying there, come what may. Even more than was the case for my other books, I wrote this like a scared rabbit.”

Ó Nualláin is a recognizable face at UC Berkeley though I think it’s fair to say that most of the faculty and PhD students couldn’t tell you who he is. To a mainstream academic, he is one of the nebulous class of people who show up to events. One glorious loophole of university culture is that the riches of intellectual communion are often made available in open seminars held by people so weary of obscurity that they are happy for any warm body that cares enough to attend. This condition combined with the city of Berkeley’s accommodating attitude towards quacks and vagrants adds flavor to the university’s intellectual character.

There is of course no campus for the University of Ireland, California. Ó Nualláin is a truly independent scholar. Unlike many more unfortunate intellectuals, he has made the brilliant decision to not quit his day job, which is as a musician. A Google inquiry into the man indicates he probably got his PhD from Dublin City University and spent a good deal of time around Stanford’s Symbolic Systems department. (EDIT: Sean has corrected me on the details of his accomplished biography in the comments.)

I got on his mailing lists some time ago because of my interest in the Foundations of Mind conference, which he runs in Berkeley. Later, I was impressed by his aggressive volley of questions when Nick Bostrom spoke at Berkeley (I’ve become familiar with Bostrom’s work through MIRI (formerly SingInst). I’ve spoken to him just a couple times, once at a poster session at the Berkeley Institute of Data Science and once at Katy Huff’s scientific technology practice group, The Hacker Within.

I’m providing these details out of what you might call anthropological interest. At the School of Information I’ve somehow caught the bug of Science and Technology Studies by osmosis. Now I work for Charlotte Cabasse on her ethnographic team, despite believing myself to be a computational social scientist. This qualitative work is a wonderful excuse to write about ones experiences.

My perceptions of Ó Nualláin are relevant, then, because they situate the author of One Magisterium as an outsider to the academic mainstream at Berkeley. This outsider status comes through quite heavily in the book, starting from the Acknowledgments section (which recognizes all the service staff at the bars and coffee shops where he wrote the book) and running as a regular theme throughout. Discontent with and rejection from academia-as-usual are articulated in sublimated form as harsh critique of the academic institution. Ó Nualláin is engaged in an “uncompromising search for truth and justice,” and the university as it exists today demands too many compromises.

Magisterium is a Catholic term for a teaching authority. One Magisterium refers to the book’s ambition of pointing to a singular teaching authority, a new one heretofore unrecognized by other teaching authorities such as mainstream universities. Hence the book is an attack on other sources of intellectual authority. An example passage:

The devastating news for any reader venturing a toe into the stormy waters of this book is that its writer’s view is that we may never be able to dignify the moral, epistemological and political miasma of the early twenty-first century with terms like “crisis” for which the appropriate solution is of course a “paradigm shift”. It may simply be a set of hideously interconnected messes; epistemological and administrative in the academy, institutional and moral in the greater society. As a consequence, the landscape of possible “solutions” may seem so unconstrained that the wisdom of Joe the barman may be seen to equal that of any series of tomes, no matter how well-researched.

This book is above all an attempt to unify the plurality of discourses — scientific, religious, moral, aesthetic, and so on — that obtain at the start of the third millenium.

An anthropologist of science might observe that this criticality-of-everything, coupled with the claim to have a unifying theory of everything, is a surefire way to get ignored by the academy. The incentive structure of the academy requires specialization and a political balance of ideas. If somebody were to show up with the right idea, it would discredit a lot of otherwise important people and put others out of a job.

The problem, or one of them (there are many mentioned in the first chapter of One Magisterium, titled “The Trouble with Everything”), is that Ó Nualláin is right. At least as far as I can tell at this point. It is not an easy book to read; it is not structured linearly so much as (I imagine, not knowing what I’m talking about) like complex Irish dancing music, with motifs repeated and encircling themselves like a double helix or perhaps some more complex structure. Threaded together are topics from Quantum Mechanics, an analysis of the anthropic principle, a critique of Dawkins’ atheism and a positioning of the relevance of Vedanta theology to understanding physical reality, and an account of the proper role of the arts in society. I suspect that the book is meant to unfold on ones psychology slowly, resulting in ones adoption of what Ó Nualláin calls bionoetics, the new united worldview that is the alleged solution to everything.

A key principle of bionoetics is the recognition of what Ó Nualláin calls the “noetic” level of description, which is distinct from the “cognitive” third-person stance in that it is compressed in a way that makes it relevant to action in any particular domain of inquiry. Most of what he describes as “noetic” I read as “phenomenological”. I wonder if Ó Nualláin has read Merleau-Ponty–he uses the Husserlian critique of “psychologism” extensively.

I think it’s immaterial whether “noetic” is an appropriate neologism for this blending of the first-personal experience into the magisterium. Indeed, there is something comforting to a hard-headed scientist about Ó Nualláin’s views: contrary to the contemporary anthropological view, this first-personal knowledge has no place in academic science; it’s place is art. Having been in enough seminars at the School of Information where anthropologists lament not being taken seriously as producing knowledge comparable to that of the Scientists, and being one who appreciates the value of Art without needing it to be Science, I find something intuitively appealing about this view. Nevertheless, one wonders if the epistemic foundation of Ó Nualláin’s critique of the academy is grounded in scientific inquiry or his own and others first-personal noetic experiences coupled with observations of who is “successful” in scientific fields.

Just one chapter into One Magisterium, I have to say I’m impressed with it in a very specific way. Some of us learn about the world with a synthetic mind, searching for the truth with as few constraints on ones inquiry as possible. Indeed, that’s how I wound up at as nebulous place as the School of Information at Berkeley. As one conducts the search, one finds oneself increasingly isolated. Some truths may never be spoken, and it’s never appropriate to say all the truths at once. This is especially true in an academic context, where it is paramount for the reputation of the institution that everyone avoid intellectual embarrassment whenever possible. So we make compromises, contenting ourselves with minute and politically palatable expertise.

I am deeply impressed that Ó Nualláin has decided to fuck all and tell it like it is.

Fascinated by Vijay Narayanan’s talk at #DataEDGE

As I write this I’m watching Vijay Narayanan’s, Director of Algorithms and Data Science Solutions at Microsoft, talk at the DataEDGE conference at UC Berkeley.

The talk is about “The Data Science Economy.” It began with a history of the evolution of the human centralized nervous system. He then went on to show the centralizing trend of the data economy. Data collection will be become more mobile, data processing will be done in the cloud. This data will be sifted by software and used to power a marketplace of services, which ultimately deliver intelligence to their users.

It was wonderful to see somebody so in the know reaffirming what has been a suspicion I’ve had since starting graduate school but have found little support for in the academic setting. The suspicion is that what’s needed to accurately model the data science economy is a synthesis of cognitive science and economics that can show the comparative market value and competitiveness of different services.

This is not out of the mainline of information technology, management science, computer science, and other associated disciplines that have been at the nexus of business and academia for 70 years. It’s an intellectual tradition that’s rooted in the 1940’s cybernetics vision of Norbert Wiener and was going strong in the social sciences as late as Beniger‘s The Control Revolution, which, like Narayanan, draws an explicit connection between information processing in the brain and information processing in the microprocessor–notably while acknowledging the intermediary step of bureaucracy as a large-scale information processing system.

There’s significant cross-pollination between engineering, economics, computer science, and cognitive psychology. I’ve read papers from, say, the Education field in the late 80’s and early 90’s that refers to this collectively as “the dominant paradigm”. At UC Berkeley today, it’s fascinating to see a departmental politics play out over ‘data science’ that echoes some of these concerns that a powerful alliance of ideas are getting mobilized by industry and governments while other disciplines are struggling to find relevance.

It’s possible that these specialized disciplinary discourses are important for the cultivation of thought that is important for its insight despite being fundamentally impractical. I’m coming to a different view: that maybe the ‘dominant paradigm’ is dominant because it is scientifically true, and that other disciplinary orientations are suffering because they are based on unsound theory. If disciplines that are ‘dominated’ by another paradigm are floundering because they are, to put it simply, wrong, then that is a very elegant explanation for what’s going on.

The ramification of this is that what’s needed is not a number of alternatives to ‘the dominant paradignm’. What’s needed is that scholars double down on the dominant paradigm and learn how to express in its logic the complexities and nuances that the other disciplines have been designed to capture. What we can hope for, in terms of intellectual continuity, is the preservation of what’s best of older ideas in a creative synthesis with the foundational principles of computer science and mathematical biology.

going post-ideology

I’ve spent a lot of my intellectual life in the grips of ideology.

I’m glad to be getting past all of that. That’s one reason why I am so happy to be part of Glass Bead Labs.

Glass Bead Labs

There are a lot of people who believe that it’s impossible to get beyond ideology. They believe that all knowledge is political and nothing can be known with true clarity.

I’m excited to have an opportunity to try to prove them wrong.

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.

a brief comment on feminist epistemology

One funny thing about having a blog is that I can tell when people are interested in particular posts through the site analytics. To my surprise, this post about Donna Haraway has been getting an increasing number of hits each month since I posted it. That is an indication that it has struck a chord, since steady exogenous growth like that is actually quite rare.

It is just possible that this means that people interested in feminist epistemology have been reading my blog lately. They probably have correctly guessed that I have not been the biggest fan of feminist epistemology because of concerns about bias.

But I’d like to take the opportunity to say that my friend Rachel McKinney has been recommending I read Elizabeth Anderson‘s stuff if I want to really get to know this body of theory. Since Rachel is an actual philosopher and I am an amateur who blogs about it on weekends, I respect her opinion on this a great deal.

So today I started reading through Anderson’s Stanford Encyclopedia of Philosophy article on Feminist Epistemology and I have to say I think it’s very good. I like her treatment of the situated knower. It’s also nice to learn that there are alternative feminist epistemologies to certain standpoint theories that I think are troublesome. In particular, it turns out that those standpoint theories are now considered by feminist philosophers to from a brief period in the 80’s that they’ve moved past already! Now subaltern standpoints are considered privileged in terms of discovery more than privileged in terms of justification.

This position is certainly easier to reconcile with computational methods. For example, it’s in a sense just mathematically mathematically correct if you think about it in terms of information gain from a sample. This principle appears to have been rediscovered in a way recently by the equity-in-data-science people when people talk about potential classifier error.

I’ve got some qualms about the articulation of this learning principle in the absence of a particular inquiry or decision problem because I think there’s still a subtle shift in the argumentation from logos to ethos embedded in there (I’ve been seeing things through the lens of Aristotelian rhetoric lately and it’s been surprisingly illuminating). I’m on the lookout for a concrete application of where this could apply in a technical domain, as opposed to as an articulation of a political affinity or anxiety in the language of algorithms. I’d be grateful for links in the comments.

Edit:

Wait, maybe I already built one. I am not sure if that really counts.

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.) 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 ones 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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