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PhD Programme
ITU  /  Research  /  PhD Programme  /  Courses  /  Archive  /  2021  /  PhD Course - Overcoming bias and calling bullshit in the age of big data

PhD Course - Overcoming bias and calling bullshit in the age of big data

Organizers:

  • Professor Thore Husfeldt
  • Anders McIlquham-Schmidt

Lecturer:

  • Professor Thore Husfeldt

Dates of the course:

October 4th – December 6th (Week 40-49)

Time: 

TBD

Room/Online:

TBD (Online or On-premises (ITU). Depending on participants)

Course description:

The course is the ITU equivalent of the “Calling Bullshit” course by Carl Bergstrom and Jevin West at University of Washington. The full course description for Bergstrom and West’s course can be found at: https://www.callingbullshit.org/syllabus.html. We will follow essentially the same schedule and reading material.

The reading material covers different aspects of bias and bullshit in various settings: social/news media, misleading/non-representative data, visual representations of data, conclusions based on machine learning/algorithms, publication bias etc. See the program for a more elaborate description of the topics.

As phrased by Bergstrom and West: 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 bias and bullshit contaminating your information diet.
  • Recognize bias whenever and wherever you encounter it.
  • 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 biased or 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.

Reading material

  • “Calling Bullshit: The art of scepticism in a data-driven world” (CB) (Bergstrom & West, Random House)

Selected chapters from

  • “Not Born Yesterday” (NBY) (Mercier, Princeton)
  • “Weapons of Math Destruction” (WMD) (O’Neill, Crown).
  • “Thinking, Fast and Slow” (TFS) (Kahneman, Brockman)

and

  • videos from the course “Calling Bullshit” at University of Washington (see this link) and other relevant videos from youtube

Selection from articles below

  • Harry Frankfurt (1986) On Bullshit. Raritan Quarterly Review 6(2)
  • Carl Sagan 1996 The Fine Art of Baloney Detection. Chapter 12 in Sagan (1996) The Demon-Haunted World
  • Gordon Pennycook et al. (2015) On the reception and detection of pseudo-profound bullshit. Judgement and Decision Making 10:549-563
  • Milton Friedman's thermostat. Selection masked as transformation.
  • Robert Matthews (2000) Storks deliver babies (p=0.008). Teaching Statistics 22:36-38
  • Alvan Feinstein et al. (1985) The Will Rogers Phenomenon — Stage Migration and New Diagnostic Techniques as a Source of Misleading Statistics for Survival in Cancer. New England Journal of Medicine 312:1604-1608.
  • Danah boyd and Kate Crawford (2011) Six Provocations for Big Data. A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society.
  • David Lazer et al. (2014) The Parable of Google Flu: Traps in Big Data analysis. Science 343:1203-1205
  • Alyin Caliskan et al. (2017) Semantics derived automatically from language corpora contain human-like biases Science 356:183-186
  • Jevin West (2014) How to improve the use of metrics: learn from game theory Nature 465: 871-872
  • John Ioannidis (2005) Why most published scientific results are false. PLOS Medicine 2: e124
  • Fake academe looking much like the real thing. New York Times Dec. 29, 2016.
  • Adam Marcus and Ivan Oransky (2016) Why fake data when you can fake a scientist? Nautilus November 24.
  • Alan Sokal (1996) A physicist experiments with cultural studies. Lingua Franca 6:62-64.
  • Jennifer Ruark (2017) Anatomy of a hoax. Chronicle of Higher Education
  • Susan Fiske (2016) Mob Rule or Wisdom of Crowds? APS Observer preliminary draft. Also read commentaries [1] and [2].
  • Michael Blatt (2016) Vigilante Science. Plant Physiology 169:907-909. Before 'Fake News' Came False Prophecy The Atlantic Monthly Dec. 27, 2016
  • Before 'Fake News' Came False Prophecy The Atlantic Monthly Dec. 27,
  • Factcheck.org: How to spot fake news
  • Inside a fake news sausage-factory: 'It's all about income' New York Times Nov. 25, 2016
  • Donath, Judith (2016) Why fake news stories thrive online. CNN Opinion.
  • Brian Feldman (2017) Google's dangerous identity crisis. New York Magazine
  • John Cook and Stephan Lewandowsky (2012) The Debunking Handbook.



Programme:

1 weekly meeting for 10 weeks. Each participant should give a short presentation of a chapter and/or article(s) during the course. The presentation is followed by a discussion in plenum.

    Prerequisites:

    None

    Exam:

    Active participation and presentation. Three-page essay on “Overcoming bias and calling bullshit in the age of big data” of your own choice.

    Credits:

    2 ECTS

    Amount of hours the student is expected to use on the course:

    Participation: 10 hours

    Preparation + exam: 55 hours

    Participants:

    10-15 mainly PhD students from ITU and other universities but also accessible for other interested persons.

    How to sign up:

    Send an email to Anders McIlquham-Schmidt to anmc@itu.dk






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