Three-Toed Sloth

Course Announcement: "Statistics of Inequality and Discrimination" (36-313)

Attention conservation notice: Advertisement for a course you won't take, at a university you don't attend. Even if the subject is of some tangential interest, why not check back in a few months to see if the teacher has managed to get himself canceled, and/or produced anything worthwhile?

In the fall I will, again, be teaching something new:

36-313, Statistics of Inequality and Discrimination
9 units
Time and place: Tuesdays and Thursdays, 1:25 -- 2:45 pm, location TBA
Description: Many social questions about inequality, injustice and unfairness are, in part, questions about evidence, data, and statistics. This class lays out the statistical methods which let us answer questions like Does this employer discriminate against members of that group?, Is this standardized test biased against that group?, Is this decision-making algorithm biased, and what does that even mean? and Did this policy which was supposed to reduce this inequality actually help? We will also look at inequality within groups, and at different ideas about how to explain inequalities between groups. The class will interweave discussion of concrete social issues with the relevant statistical concepts.
Prerequisites: 36-202 ("Methods for Statistics and Data Science") (and so also 36-200, "Reasoning with Data")

This is a class I've been wanting to teach for some years now, and I'm very happy to finally get the chance to feel my well-intentioned but laughably inadequate efforts crushed beneath massive and justified opprobrium evoked from all sides bore and perplex some undergrads who thought they were going to learn something interesting in stats. class for a change try it out.

Tentative topic schedule

About one week per.

1. "Recall": Reminders about probability and statistics: populations, distribution within a population, distribution functions, joint and conditional probability; samples and inference from samples. Reminders (?) about social concepts: ascriptive and attained social categories; status, class, race, caste, sex, gender, income, wealth.
2. Income and wealth inequality: What does the distribution of income and wealth look like within a population? How do we describe population distributions, especially when there is an extreme range of values (a big difference between the rich and poor)? Where does the idea of "the 1%" wealthy elite come from? How has income inequality changed over recent decades?
   Statistical tools: measures of central tendency (median, mode, mean), of dispersion, and of skew; the concept of "heavy tails" (the largest values being orders of magnitude larger than typical values); log-normal and power law distributions; fitting distributions to existing data; positive feedback, multiplicative growth and "cumulative advantage" processes.
3. Income disparities: How does income (and wealth) differ across groups? How do we compare average or typical values? How do we compare entire distributions? How have income inequalities by race and sex changed over recent decades?
   Statistical tools: permutation tests for differences in mean (and other measures of the average); two-sample tests for differences in distribution; inverting tests to find the range of differences compatible with the data; the "analysis of variance" method of comparing populations; the "relative distribution" method of comparing populations
4. Detecting discrimination in hiring: Do employers discriminate in hiring (or schools in admission, etc.)? How can we tell? When are differences in hiring rates evidence for discrimination? How do statistical perspectives on this question line up with legal criteria for "disparate treatment" and "disparate impact"?
   Statistical tools: tests for differences in proportions or probabilities; adjusting for applicant characteristics; deciding what to adjust for
5. Detecting discrimination in policing: Do the police discriminate against members of particular racial groups? When do differences in traffic stops, arrests, or police-caused deaths indicate discrimination? Does profiling or "statistical discrimination" make sense for the police? Can groups be simultaneously be over- and under- policed?
   Statistical tools: test for differences in proportions; signal detection theory; adjusting for systematically missing data; self-reinforcing equilibria
6. Algorithmic bias: Can predictive or decision-making algorithms be biased? What would that even mean? Do algorithms trained on existing data necessarily inherit the biases of the world? What notions of fairness or unbiased can we actually implement for algorithms? What trade-offs are involved in enforcing different notions of fairness? Are "risk-prediction instruments" fair?
   Statistical tools: Methods for evaluating the accuracy of predictions; differential error rates across groups; decision trees; optimization and multi-objective optimization.
7. Standardized tests: Are standardized tests for school admission biased against certain racial groups? What does it mean to measure qualifications, and how would we know whether tests really are measuring qualifications? What does it mean for a measurement to be biased? When do differences across groups indicate biases? (Disparate impact again.) Why correlating outcomes with test scores among admitted students may not make sense. The "compared to what?" question.
   Statistical tools: Predictive validity; differential prediction; "conditioning on a collider"
8. Intelligence tests: Are intelligence tests biased? How do we measure latent attributes? How do we know the latent attributes even exist? What would it mean for there to be such a thing as "general intelligence", that could be measured by tests? What, if anything, do intelligence tests measure? What rising intelligence test results (the Flynn Effect) tell us?
   Statistical tools: correlation between test scores; factor models as an explanation of correlations; estimating factor values from tests; measurement invariance; alternatives to factor models
9. Implicit bias: Do "implicit association tests" measure unconscious biases? Again on measurement, as well as what it would mean for a bias to be "implicit" or "unconscious". What, if anything, do implicit association tests measure?
   Statistical tools: Approaches to "construct validity".
10. Interventions on implicit bias: Can trainings or other interventions reduce implicit bias? How do we investigate the effectiveness of interventions? How do we design a good study an intervention? How do we pool information from multiple studies. Do implicit bias interventions change behavior? Does having a chief diversity officer increase faculty diversity?
    Statistical tools: Experimental design: selecting measurements of outcomes, and the importance of randomized studies; meta-analytic methods for combining information.
11. Explaining, or explaining away, inequality: To what extent can differences in outcomes between groups be explained by differences in their attributes (e.g., explaining differences in incomes by differences in marketable skills)? How should we go about making such adjustments? Is it appropriate to treat discrimination as the "residual" left unexplained? When does adjusting or controlling for a variable contribute to an explanation, and when is it "explaining away" discrimination? What would it mean to control for race, sex or gender?
    Statistical tools: Observational causal inference; using regression to "control for" multiple variables at once; using graphical models to represent causal relations between variables; how to use graphical models to decide what should and what should not be controlled for; the causal model implicit in decisions about controls.
12. Self-organizing inequalities and "structural" or "systematic" inequalities: Models of how inequalities can perpetuate themselves even when nobody is biased. Models of how inequalities can appear even when nobody is biased. The Schelling model of spatial segregation as a "paradigm". How relevant are Schelling-type models to actual, present-day inequalities?
    Statistical tools: Agent-based models; models of social learning and game theory.
13. Statistics and its history: The development of statistics in the 19th and early 20th century was intimately tied to the eugenics movement, which was deeply racist and even more deeply classist, but also often anti-sexist. The last part of the course will cover this history, and explain how many of the intellectual tools we have gone over to document, and perhaps to help combat, inequality and discrimination were invented by people who wanted to use them for quite different purposes. The twin learning objectives for this section are for students to grasp something of this history, and to grasp why the "genetic fallacy", of judging ideas by where they come from (their "genesis") is, indeed, foolish and wrong.
    Statistical tools: N/A.

Evaluation

There will be one problem set per week; each of these homeworks will involve some combination of (very basic) statistical theory, (possibly less basic) calculations using the theory we've gone over, and analysis of real data sets using the methods discussed in class. There will also be readings for each class session, and a short-answer quiz after each session will combine questions based on lecture content with questions based on the readings.

There will not be any exams.

My usual policy is to drop a certain number of homeworks, and a certain number of lecture/reading questions, no questions asked. The number of automatic drops isn't something I'll commit to here and now (similarly, I won't make any promises here about the relative weight of homework vs. lecture-related questions).

Textbook, Lecture Notes

There is, unfortunately, no one textbook which covers the material we'll go over at the required level. You will, instead, get very detailed lecture notes after each lecture. There will also be a lot of readings from various books and articles. (I will not agree with every reading I assign.)

Corrupting the Young; Enigmas of Chance; Commit a Social Science