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?

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 algorit