• Groups: Final project teams (if team members here)
• Respond to worksheet questions on final projects

• Warm-up: final project prep
• Data models vs. algorithmic models
• Activity: categorization by hand
• Unsupervised algorithms, supervised algorithms
• Reflection on algorithms, society

• Machine learning with tidymodels
• Guest speaker from Recidiviz - add your questions on “Collaborations” tab

• Office Hours: Monday 1:30-3:30pm (Tyler)
• Tuesdays, 10:30am-12:00pm (Yao)

• Motivate algorithmic approaches to social science
• Explore classification strategies
• Understand data models vs. algorithms
• Understand supervised vs. unsupervised algorithms
• Discuss algorithmic bias
• (Wednesday) Machine Learning with tidymodels

• What is an example of a social process where something (\(x\)) leads to something else (\(y\))?

Source: Breiman, 2002

• In the data modeling approach, we would use a combination of features \(x\) to explain how \(y\) happens
• Examples of data models?

Source: Breiman, 2002

• In the data modeling approach, we would use a combination of features \(x\) to explain how \(y\) happens
• Examples of data models?

Source: Breiman, 2002

• In the algorithmic approach, we may or may not care how \(y\) happens
• We use a machine learning model to take features from \(x\) and predict \(y\)
• Examples of algorithmic models?

Source: Breiman, 2002

• In the algorithmic approach, we may or may not care how \(y\) happens
• We use a machine learning model to take features from \(x\) and predict \(y\)
• Examples of algorithmic models?

Source: Breiman, 2002

• Unsupervised machine learning means using an algorithm to classify data where we have no ground truth
• In other words, we want to categorize our data, often on many characteristics at once, or something that is difficult to define

• Classify the following housing data into “neighborhoods”
• Outline your process: how did you classify these house?
• Is there a mathematical formula or set of instructions that could be applied to a different area?

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• In unsupervised machine learning, we have no “ground truth” answers
• We look to the algorithm for classifications

1. Pick k points (centroids) at random
2. Assign all data points to closest centroid
3. Re-calculate centroids, based on points in group
4. Re-assign points to nearest centroid
5. Repeat 3-4 until no points switch groups

• In supervised machine learning, we have “ground truth” data
• We want to train an algorithm to make predictions on new data

• Recall: We might want to split our data into training and test sets
• Question: Why do we split?

• We might even want to split up the training sample to create better models

• A decision tree uses “split points” to make decisions based on certain variables
• Let’s look at an example

• Let’s say we have the “true” neighborhood classifications
• What are some features that might help us classify neighborhoods?
• Can you think of any “decision points”?

• As our modeling strategies get more complicated, what happens to interpretability?
• Plot the following on the chart below: - decision trees, random forests, linear regression, logistic regression, neural networks, and any other modeling strategies you can think of!

• Maybe you have something like this? (not exact, will vary heavily by model specifications)
• The takeaway: in effforts to increase predictive accuracy, models can become opaque/unclear

• Recall: data models vs algorithmic models
• What would it mean if important societal decisions were made by algorithms?

• We’ve seen how algorithms can make predictions by finding hidden patterns in data
• Bias vs. variance tradeoff (statistical bias/variance)
• This can also mean social bias: algorithms might favor certain individuals/groups/places

• Credit scoring is the “paradigmatic example of algorithmic governance” (Kiviat, 2019)
• Big financial data is used to predict loan repayment based on obscure patterns
• These scores dictate who can access loans, repayment rates, and more

• Example: Credit scores are biased according to individuals’ zip codes
• Question: how would you make credit scores more fair?

• Question: how would you make credit scores more fair?
• Use a data model? (Possibly more fair, but with lower predictive accuracy)
• Remove zip code? (Other data might be correlated with zip code)
• Set demographic parity (e.g. each zip code must have similar average scores)? This could make another variable more biased
• Set equal opportunity (e.g. ensure hypothetical individuals with similar characteristics have similar scores across zip codes if all else equal)?

• Question: how would you make credit scores more fair?
• This is not only a modeling question, it is also a social question!
• Requires theory of fairness in society

• Unsupervised algorithm: create categorizations when we don’t have any (e.g. k-means)
• Supervised algorithm: make predictions when we have some ground-truth data (e.g. random forest)
• Data models: explainable, interpretable, not necessarily the best predictors
• Algorithmic approaches: accurate predictions, not necessarily explainaable/interpretable
• Algorithmic fairness: requires social theory and algorithmic knowledge

• Respond to the prompts on the back of the warm-up exercise.