ST313 course page
Ethics for data science: the statistical choices hidden inside technical steps, and who they affect.
Each week has one lecture and one class (80 minutes each, lecture first). The notes are the main reference; the slides are what is shown in the lecture; the class sheet is what you work through in class, and its .qmd link downloads the source to run the R code. Bring a laptop with R and RStudio to the class.
Week 1: Selection and the replication crisis
Why a literature of honest tests can still be wrong on average: power, the significance filter, and what a published estimate is conditioned on. The standing question, with two cases.
Reading: ms4ds, chapter 3: Ethical data science, sections 3.1 to 3.6 · Wikipedia: Replication crisis, skim
Class: class sheet (.qmd) · class sheet with solutions
Week 2: Mindless statistics and sophisticated bigotry
Cargo cult science, cargo-cult statistics and the null ritual, and Feynman’s first principle: you must not fool yourself. Then numbers that are computed correctly and read as answers to other questions: a correlation across fifty states with the opposite sign among the people who live in them, what \(R^2\) measures and what averaging does to it, decisions about a person made from the average of their group, what a small difference in spread does far out in a tail, and what the largest of \(n\) items shows you of a group.
Required reading: Stark and Saltelli, Cargo-cult statistics and scientific crisis (in full) · Hyde and Mertz, Gender, culture, and mathematics performance (first paragraph of “Do Gender Differences Exist Among the Mathematically Talented?”, Figure 1, Table 2; free full text) · Freedman, Ecological Inference and the Ecological Fallacy (sections 1 to 3) · one of two psychology studies, as announced (Connellan and colleagues 2000, or Alexander and colleagues 2009), skimmed for its data and analysis
Optional: Rathje, Van Bavel and van der Linden, Out-group animosity drives engagement on social media · Gigerenzer and Marewski, Surrogate Science
Class: class sheet (.qmd) · class sheet with solutions
Week 3: Ethics and causation
Four frameworks of normative ethics for judging whether an action is right, utilitarianism and its difficulties, the applied principles written after the Tuskegee study, and the five Cs, a practical checklist for data work. A consequentialist judges an action by what it brings about, which is a causal question: how much does a job-search course help people find work, when the people who attend it are more likely to find work anyway? Causal graphs, conditioning and intervening, the average treatment effect and what an average hides, confounding and how to adjust for it, and the choice of graph that any adjustment rests on.
Required reading: Barocas, Hardt and Narayanan, Fairness and Machine Learning, chapter 5: Causality (“The limitations of observation” through “Confounding”, pages 105 to 116) · ms4ds, chapter 3: Ethical data science (sections 3.2 and 3.3, a re-read) · skim the Wikipedia articles Consequentialism, Deontology, Virtue ethics, Pragmatic ethics and Belmont Report
Optional: Cunningham, Causal Inference: The Mixtape, first edition (section 1.2 and sections 3.1 to 3.1.4) · Tukey, Conclusions vs Decisions · Bross, The Role of the Statistician: Scientist or Shoe Clerk
Class: class sheet (.qmd)
Week 4: Scores and what they stand for
A score measures one thing and is taken to stand for another. A hospital’s risk score predicted next year’s medical cost and was used to decide who got extra care; at the same cost, Black patients were sicker, so the score enrolled fewer of them. A model of a measure with an offset for one group, who it selects and how ill they are, and a noisy measure that flattens a slope. Reinforcement learning: an agent given a reward, and the good we care about. When a preference has a number, what a ranking of everyone assumes, and why “better for this person” needs no ranking. What happens once a score is rewarded: Goodhart’s law as an intervention, strategic classification, performative prediction, and a platform that ranked by views.
Required reading: Obermeyer, Powers, Vogeli and Mullainathan, Dissecting racial bias in an algorithm used to manage the health of populations (Science, 2019; read the body, not only the abstract) · Barocas, Hardt and Narayanan, Fairness and Machine Learning, chapter 1: Introduction (“The trouble with measurement” through “Feedback and feedback loops”, pages 7 to 14) · skim the Wikipedia article Goodhart’s law
Optional: Amodei and colleagues, Concrete Problems in AI Safety (sections 2 and 4) · YouTube Creator Blog, why we focus on watch time (2012) · Miller, Milli and Hardt, Strategic Classification is Causal Modeling in Disguise (section 1) · Perdomo, Zrnic, Mendler-Dünner and Hardt, Performative Prediction (section 1) · Jacobs and Wallach, Measurement and Fairness · Manheim and Garrabrant, Categorizing Variants of Goodhart’s Law · Fourcade and Healy, Classification situations
Bring to the class: one score or algorithm used to make decisions about people, with what it measures and what it stands in for.