Calling Bullshit in the Age of Big Data by Carl T. Bergstrom and Jevin West.
From the about page:
The world is awash in bullshit. Politicians are unconstrained by facts. Science is conducted by press release. So-called higher education often rewards bullshit over analytic thought. Startup culture has elevated bullshit to high art. Advertisers wink conspiratorially and invite us to join them in seeing through all the bullshit, then take advantage of our lowered guard to bombard us with second-order bullshit. The majority of administrative activity, whether in private business or the public sphere, often seems to be little more than a sophisticated exercise in the combinatorial reassembly of bullshit.
We’re sick of it. It’s time to do something, and as educators, one constructive thing we know how to do is to teach people. So, the aim of this course is to help students navigate the bullshit-rich modern environment by identifying bullshit, seeing through it, and combatting it with effective analysis and argument.
What do we mean, exactly, by the term bullshit? As a first approximation, bullshit is language intended to persuade by impressing and overwhelming a reader or listener, with a blatant disregard for truth and logical coherence.
While bullshit may reach its apogee in the political sphere, this isn’t a course on political bullshit. Instead, we will focus on bullshit that comes clad in the trappings of scholarly discourse. Traditionally, such highbrow nonsense has come couched in big words and fancy rhetoric, but more and more we see it presented instead in the guise of big data and fancy algorithms — and these quantitative, statistical, and computational forms of bullshit are those that we will be addressing in the present course.
Of course an advertisement is trying to sell you something, but do you know whether the TED talk you watched last night is also bullshit — and if so, can you explain why? Can you see the problem with the latest New York Times or Washington Post article fawning over some startup’s big data analytics? Can you tell when a clinical trial reported in the New England Journal or JAMA is trustworthy, and when it is just a veiled press release for some big pharma company?
Our aim in this course is to teach you how to think critically about the data and models that constitute evidence in the social and natural sciences.
Learning Objectives
Our learning objectives are straightforward. After taking the course, you should be able to:
- Remain vigilant for bullshit contaminating your information diet.
- Recognize said bullshit whenever and wherever you encounter it.
- Figure out for yourself precisely why a particular bit of bullshit is bullshit.
- Provide a statistician or fellow scientist with a technical explanation of why a claim is bullshit.
- Provide your crystals-and-homeopathy aunt or casually racist uncle with an accessible and persuasive explanation of why a claim is bullshit.
We will be astonished if these skills do not turn out to be among the most useful and most broadly applicable of those that you acquire during the course of your college education.
A great syllabus and impressive set of readings, although I must confess my disappointment that Is There a Text in This Class? The Authority of Interpretive Communities and Doing What Comes Naturally: Change, Rhetoric, and the Practice of Theory in Literary and Legal Studies, both by Stanley Fish, weren’t on the list.
Bergstrom and West are right about the usefulness of this “class” but I would use Fish and other literary critics to push your sensitivity to “bullshit” a little further than the readings indicate.
All communication is an attempt to persuade within a social context. If you share a context with a speaker, you are far more likely to recognize and approve of their use of “evidence” to make their case. If you don’t share such a context, say a person claiming a particular interpretation of the Bible due to divine revelation, their case doesn’t sound like it has any evidence at all.
It’s a subtle point but one known in the legal, literary and philosophical communities for a long time. That it’s new to scientists and/or data scientists speaks volumes about the lack of humanities education in science majors.