To say that facts are social constructions doesn’t mean everything put forward as a fact is a fact. Nor does it mean that facts don’t express truths or facts are not to be trusted. Nor does it mean that there’s some unconstructed fact behind facts. Social constructionists don’t want to leave us in a world in which it’s ok to sat “No, it’s not raining” in the middle of a storm or claim “Water boiled at 40C for me this morning under normal circumstances.” 

Rather the critique, as I understand it, is that the fact-based disciplines we choose to pursue, the roles they play, who gets to participate, the forms of discourse and of proof, the equipment invented and the ways the materials are handled (the late Bruno Latour was brilliant on this point, among others), the commitment to an objective and consistent methodology (see Paul Feyerabend), all are the result of history, culture, economics, and social forces. Science itself is a social construct (as per Thomas Kuhn‘s The Structure of Scientific Revolutions [me on that book]). (Added bonus: Here’s Richard Rorty’s review of Ian Hacking’s excellent book, The Social Construction of What?)

Facts as facts pretty clearly seem (to me) to be social constructions. As such, they have a history…

Facts as we understand them became a thing in western culture when Francis Bacon early in the 17th century started explicitly using them to ground theories, which was a different way of constructing scientific truths; prior to this, science was built on deductions, not facts. (Pardon my generalizations.)

You can see the movement from deductive truth to fact-based empirical evidence across the many editions of Thomas Malthus‘ 1798 book,  An Essay on the Principle of Population, that predicted global famine based on a mathematical formula, but then became filled with facts and research from around the world. It went from a slim deductive volume to six volumes thick with facts and stats. Social construction added pounds to his Malthus’ work.

This happened because statistics arrived in Britain, by way of Germany, in the early 19th century. Statistical facts became important at that time not only because they enabled the inductive grounding of theories (as per Bacon and Malthus), but because they could rebut people’s personal interests. In particular,  they became an important way to break the sort of class-based assumptions that made it seem to t be ok to clean rich people’s chimneys by shoving little boys up them.  Against this were posed facts that showed that it was in fact bad for them. 

Compiling “blue books” of fact-based research became a standard part of the legislative process in England in the first half of the 19th century. By mid-century, the use of facts was so prevalent that in 1854 Dickens bemoaned society’s reliance on them in Hard Times on the grounds that facts kill imagination…yet another opposite to facts, and another social construction.

As the 19th century ended, we got our first fact-finding commissions that were established in order to peacefully resolve international disputes. (Narrator: They rarely did.) This was again using facts as the boulder that stubs your toe of self-interest (please forget I ever wrote that phrase), but now those interests were cross-national and not as easily resolvable as when you poise the interests of lace-cuffed lords against the interests of children crawling through upperclass chimneys.

In the following century  we got (i.e., we constructed) an idea of science and human knowledge that focused on assembling facts as if they were bricks out of which one could build a firm foundation. This led to some moaning (in a famous 1963 letter to the editor)  that science was turning into a mere “brickyard” of unassembled facts.

I’m not a historian, and this is the best I can recall from a rabbit hole of specific curiosity I fell into about 10 yrs ago when writing Too Big to Know. But the point is the the idea of the social construction of science and facts doesn’t mean that all facts — including “alternative facts” — are equal.  Water really does boil at 100C. Rather it’s the idea, role, use, importance, and control of facts that’s socially constructed.

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Notes for a post:

Plato said (Phaedrus, 265e) that we should “carve nature at its joints,” which assumes of course that nature has joints, i.e., that it comes divided in natural and (for the Greeks) rational ways. (“Rational” here means something like in ways that we can discover, and that divide up the things neatly, without overlap.)

For Aristotle, at least in the natural world those joints consist of the categories that make a thing what it is, and that make things knowable as those things.

To know a thing was to see how it’s different from other things, particularly (as per Aristotle) from other things that they share important similarities with: humans are the rational animals because we share essential properties with other animals, but are different from them in our rationality.

The overall order of the universe was knowable and formed a hierarchy (e.g. beings -> animals -> vertebrates -> upright -> rational) that makes the differences essential. It’s also quite efficient since anything clustered under a concept, no matter how many levels down, inherits the properties of the higher level concepts.

We no longer believe that there is a perfect, economical order of things. “We no longer believe that there is a single, perfect, economical order of things. ”We want to be able to categorize under many categories, to draw as many similarities and differences as we need for our current project. We see this in our general preference for search over browsing through hierarchies, the continued use of tags as a way of cutting across categories, and in the rise of knowledge graphs and high-dimensional language models that connect everything every way they can even if the connections are very weak.

Why do we care about weak connections? 1. Because they are still connections. 2. The Internet’s economy of abundance has disinclined us to throw out any information. 3. Our new technologies (esp. machine learning) can make hay (and sometimes errors) out of rich combinations of connections including those that are weak.

If Plato believed that to understand the world we need to divide it properly — carve it at its joints — knowledge graphs and machine learning assume that knowledge consists of joining things as many different ways as we can.

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danah boyd’s 2018 “You think you want media literacy, do you?” remains an essential, frame-changing discussion of the sort of media literacy that everyone, including danah [@zephoria], agrees we need: the sort that usually focuses on teaching us how to not fall for traps and thus how to disbelieve. But, she argues, that’s not enough. We also need to know how to come to belief.

I went back to danah’s brilliant essay because Barbara Fister [@bfister], a librarian I’ve long admired, has now posted “Lizard People in the Library.” Referencing danah’s essay among many others, Barbara asks: Given the extremity and absurdity of many American’s beliefs, what’s missing from our educational system, and what can we do about it? Barbara presents a set of important, practical, and highly sensible steps we can take. (Her essay is part of the Project Information Literacy research program.)

The only thing I’d dare to add to either essay — or more exactly, an emphasis I would add — is that we desperately need to learn and teach how to come to belief together. Sense-making as well as belief-forming are inherently collaborative projects. It turns out that without explicit training and guidance, we tend to be very very bad at it.

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Brink has just posted a piece of mine that suggests that the Internet and machine learning have been teaching companies that our assumptions about the predictability of the future — based in turn on assumptions about the law-like and knowable nature of change — don’t hold. But those are the assumptions that have led to the relatively recent belief in the efficacy of strategy.

My article outlines some of the ways organizations are facing the future differently. And, arguably, more realistically.

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The Democrats are trying to belittle the concept of a Wall, calling it old fashioned. The fact is there is nothing else’s that will work, and that has been true for thousands of years. It’s like the wheel, there is nothing better. I know tech better than anyone, & technology…..

— Donald J. Trump (@realDonaldTrump) December 21, 2018

This comes from a man who does not know how to close an umbrella.

Does Trump really believe that he knows more about tech than anyone? Even if we take away the hyperbole, does he think he’s an expert at technology? What could he mean by that? That he knows how to build a computer? What an Internet router does? That he can explain what an adversarial neural network is, or just the difference between machine learning and deep learning? That he can provide IT support when Jared can’t find the song he just downloaded to his iPhone? That he can program his VCR?

But I don’t think he means any of those things by his ridiculous claim.

I think it’s worse than that. The phrase is clearly intended to have an effect, not to mean anything. “Listen to me. Believe me.” is an assertion of authority intended to forestall questioning. A genuine expert might say something like that, and at least sometimes it’d be reasonable and acceptable; it’s also sometimes obnoxious. Either way, “I know more about x than anyone” is a conversational tool.

So, Trump has picked up a hammer. His hand is clasped around its handle. He swings his arm and brings the hammer squarely down on the nail. He hears the bang. He has wielded this hammer successfully.

Except the rest of us can see there is nothing — nothing — in his hand. We all know that. Only he does not.

Trump is not lying. He is insane.

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For the past six months I’ve been a writer in residence embedded in a machine learning research group — PAIR (People + AI Research) — at the Google site in Cambridge, MA. I was recently renewed for another 6 months.

No, it’s not clear what a “writer in residence” does. So, I’ve been writing occasional posts that try to explain and contextualize some basic concepts in machine learning from the point of view of a humanities major who is deeply lacking the skills and knowledge of a computer scientist. Fortunately the developers at PAIR are very, very patient.

Here are three of the posts:

Machine Learning’s Triangle of Error: “…machine learning systems ‘think’ about fairness in terms of three interrelated factors: two ways the machine learning (ML) can go wrong, and the most basic way of adjusting the balance between these potential errors.”

Confidence Everywhere!: “… these systems are actually quite humble. It may seem counterintuitive, but we could learn from their humility.”

Hashtags and Confidence: “…in my fever dream of the future, we routinely say things like, “That celebrity relationship is going to last, 0.7 for sure!” …Expressions of confidence probably (0.8) won’t take exactly that form. But, then, a decade ago, many were dubious about the longevity of tagging…”

I also wrote about five types of fairness, which I posted about earlier: “…You appoint five respected ethicists, fairness activists, and customer advocates to figure out what gender mix of approved and denied applications would be fair. By the end of the first meeting, the five members have discovered that each of them has a different idea of what’s fair…”

I’ve also started writing an account of my attempt to write my very own machine learning program using TensorFlow.js: which lets you train a machine learning system in your browser; TensorFlow.js is a PAIR project. This project is bringing me face to face with the details of implementing even a “Hello, world”-ish ML program. (My project aims at suggesting tags for photos, based on a set of tagged images (Creative Commons-ed) from Flickr. It’s a toy, of course.)

I have bunch of other posts in the pipeline, as well as a couple of larger pieces on larger topics. Meanwhile, I’m trying to learn as much as I possibly can without becoming the most annoying person in Cambridge. But it might be too late to avoid that title…

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I’ve written before about the need to teach The Kids (also: all of us) not only how to think critically so we can see what we should not believe, but also how to come to belief. That piece, which I now cannot locate, was prompted by danah boyd’s excellent post on the problem with media literacy. Robert Berkman, Outreach, Business Librarian at the University of Rochester and Editor of The Information Advisor’s Guide to Internet Research, asked me how one can go about teaching people how to come to belief. Here’s an edited version of my reply:

I’m afraid I don’t have a good answer. I actually haven’t thought much about how to teach people how to come to belief, beyond arguing for doing this as a social process (the ol’ “knowledge is a network” argument :) I have a pretty good sense of how *not* to do it: the way philosophy teachers relentlessly show how every proposed position can be torn down.

I wonder what we’d learn by taking a literature course as a model — not one that is concerned primarily with critical method, but one that is trying to teach students how to appreciate literature. Or art. The teacher tries to get the students to engage with one another to find what’s worthwhile in a work. Formally, you implicitly teach the value of consistency, elegance of explanation, internal coherence, how well a work clarifies one’s own experience, etc. Those are useful touchstones for coming to belief.

I wouldn’t want to leave students feeling that it’s up to them to come up with an understanding on their own. I’d want them to value the history of interpretation, bringing their critical skills to it. The last thing we need is to make people feel yet more unmoored.

I’m also fond of the orthodox Jewish way of coming to belief, as I, as a non-observant Jew, understand it. You have an unchanging and inerrant text that means nothing until humans interpret it. To interpret it means to be conversant with the scholarly opinions of the great Rabbis, who disagree with one another, often diametrically. Formulating a belief in this context means bringing contemporary intelligence to a question while finding support in the old Rabbis…and always always talking respectfully about those other old Rabbis who disagree with your interpretation. No interpretations are final. Learned contradiction is embraced.

That process has the elements I personally like (being moored to a tradition, respecting those with whom one disagrees, acceptance of the finitude of beliefs, acceptance that they result from a social process), but it’s not going to be very practical outside of Jewish communities if only because it rests on the acceptance of a sacred document, even though it’s one that literally cannot be taken literally; it always requires interpretation.

My point: We do have traditions that aim at enabling us to come to belief. Science is one of them. But there are others. We should learn from them.

TL;DR: I dunno.

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I received this about Too Big to Know from Isaiah Hoogendyk, Biblical Data Engineer at Faithlife Corporation:

In chapter 9, “Building the New Infrastructure of Knowledge,” (sorry, don’t have a page number: read this in the Kindle app) you state:

“There was a time when we thought we were doing the common folk a favor by keeping the important knowledge out of their reach. That’s why the Pope called John Wycliffe a heretic in the fourteenth century for creating the first English-language translation of the Christian Bible.”

This is quite false, actually. There was in fact nothing heretical about translating the Scriptures into the vernacular; instead, Wycliffe was condemned for a multitude of heresies regarding rejection of Catholic belief on the Sacraments and the priesthood, among other things. Some of these beliefs were interpolated into the translation of the Scriptures attributed to him (which weren’t even entirely translated by him), but it was mostly his other writings that were censured by the Pope. You can read more about that here: https://plato.stanford.edu/archives/win2011/entries/wyclif/.

Thanks, Isaiah.

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I’m at the STEAM ed Finland conference in Jyväskylä. Harri Ketamo is giving a talk on “micro-learning.” He recently won a prestigious prize for the best new ideas in Finland. He is interested in the use of AI for learning.

We don’t have enough good teachers globally, so we have to think about ed in new ways, Harri says. Can we use AI to bring good ed to everyone without hiring 200M new teachers globally? If we paid teachers equivalent to doctors and lawyers, we could hire those 200M. But we apparently not willing to do that.

One challenge: Career coaching. What do you want to study? Why? What are the skills you need? What do you need to know?

His company does natural language analysis — not word matches, but meaning. As an example he shows a shareholder agreement. Such agreements always have the same elements. After being trained on law, his company’s AI can create a map of the topic and analyze a block of text to see if it covers the legal requirements…the sort of work that a legal assistant does. For some standard agreements, we may soon not need lawyers, he predicts.

The system’s language model is a mess of words and relations. But if you zoom out from the map, the AI has clustered the concepts. At the Slush Sanghai conference, his AI could develop a list of the companies a customer might want to meet based on a text analysis of the companies’ web sites, etc. Likewise if your business is looking for help with a project.

Finland has a lot of public data about skills and openings. Universities’ curricula are publicly available.[Yay!] Unlike LinkedIn, all this data is public. Harri shows a map that displays the skills and competencies Finnish businesses want and the matching training offered by Finnish universities. The system can explore public information about a user and map that to available jobs and the training that is required and available for it. The available jobs are listed with relevancy expressed as a percentage. It can also look internationally to find matches.

The AI can also put together a course for a topic that a user needs. It can tell what the core concepts are by mining publications, courses, news, etc. The result is an interaction with a bot that talks with you in a Whatsapp like way. (See his paper “Agents and Analytics: A framework for educational data mining with games based learning”). It generates tests that show what a student needs to study if she gets a question wrong.

His newest project, in process: Libraries are the biggest collections of creative, educational material, so the AI ought to point people there. His software can find the common sources among courses and areas of study. It can discover the skills and competencies that materials can teach. This lets it cluster materials around degree programs. It can also generate micro-educational programs, curating a collection of readings.

His platform has an open an API. See Headai.

Q&A

Q: Have you done controlled experiments?

A: Yes. We’ve found that people get 20-40% better performance when our software is used in blended model, i.e., with a human teacher. It helps motivate people if they can see the areas they need to work on disappear over time.

Q: The sw only found male authors in the example you put up of automatically collated materials.

A: Small training set. Gender is not part of the metadata in Finland.

A: Don’t you worry that your system will exacerbate bias?

Q: Humans are biased. AI is a black box. We need to think about how to manage this

Q: [me] Are the topics generated from the content? Or do you start off with an ontology?

A: It creates its ontology out of the data.

Q: [me] Are you committing to make sure that the results of your AI do not reflect the built in biases?

A: Our news system on the Web presents a range of views. We need to think about how to do this for gender issues with the course software.

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The Wall Street Journal has run an article by Robert Lee Hotz that gently ridicules scientists for including thousands of people as co-authors of some scientific publications. Sure, a list of 2,000 co-authors is risible. But the article misses some of the reasons why it’s not.

As Robert Lee points out, “experiments have gotten more complicated.” But not just by a little. How many people did it take to find the Higgs Boson particle? In fact, as Michael Nielsen (author of the excellent Reinventing Discovery) says, how many people does it take to know that it’s been found? That knowledge depends on deep knowledge in multiple fields, spread across many institutions and countries.

In 2012 I liveblogged a fantastic talk by Peter Galison on this topic. He pointed to an additional reason: it used to be that engineers were looked upon as mere technicians, an attitude mirrored in The Big Bang (the comedy show, not the creation of the universe—so easy to get those two confused!). Over time, the role of engineers has been increasingly appreciated. They are now often listed as co-authors.

In an age in which knowledge quite visibly is too big to be known by individuals, sharing credit widely more accurate reflects its structure.

In fact, it becomes an interesting challenge to figure out how to structure metadata about co-authors so that it captures more than name and institution and does so in ways that make it interoperable. This is something that my friend Amy Brand has been working on. Amy, recently named head of the MIT University Press is going to be a Berkman Fellow this year, so I hope this topic will be a subject of discussion at the Center.

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