8. Recipes

And old adage says: garbage in, garbage out. Here we avoid garbage in.

Figure 1: Artwork by @allison_horst.

Agenda

October 21, 2021

1. What needs to be done to the data?
2. tidymodels syntax for recipes
3. Example

Readings

• Class notes: k nearest neighbors

• Max Kuhn and Julia Silge (2021), Tidy Modeling with R

Reflection questions

• What is the process for building a model using tidymodels?

• Why is it important to do feature engineering for variables in a model?

• How is data separated in order to work with independent information (hint: two ways)?

Ethics considerations

• There are two ways that laws are enforced (both equally important):

  1. disparate treatment \(\rightarrow\) means that the differential treatment is intentional

  2. disparate impact \(\rightarrow\) means that the differential treatment is unintentional or implicit (some examples include advancing mortgage credit, employment selection, predictive policing)

• Anti-discrimination Laws

  • Civil Rights Acts of 1964 and 1991
  • Americans with Disabilities Act
  • Genetic Information Nondiscrimination Act
  • Equal Credit Opportunity Act
  • Fair Housing Act

• Questions to ask yourself in every single data analysis you perform (taken from Data Science for Social Good at the University of Chicago):

  • What biases may exist in the data you’ve been given? How can you find out?
  • How will your choices with tuning parameters affect different populations represented in the data?
  • How do you know you aren’t getting the right answer to the wrong question?
  • How would you justify what you’d built to someone whose welfare is made worse off by the implementation of your algorithm?
  • See the slides on bias in modeling (9/23/21) for times when there are no inherent biases but the structure of the data create unequal model results.

Slides

• In class slides for 10/21/21.

• WU12 - feature engineering

Additional Resources

• Hilary Mason describing what is machine learning to 5 different people.

• Julia Silge’s blog is full of complete tidymodels examples and screencasts.

• Alexandria Ocasio-Cortez, Jan 22, 2019 MLK event with Ta-Nehisi Coates

• S. Barocas and A. Selbst, “Big Data’s Disparate Impact”, California Law Review, 671, 2016.

• Machine Bias in Pro Publica by Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner, May 23, 2016

• Algorithmic Justice League is a collective that aims to:

  • Highlight algorithmic bias through media, art, and science

  • Provide space for people to voice concerns and experiences with coded bias

  • Develop practices for accountability during design, development, and deployment of coded systems

  • Joy Buolamwini – AI, Ain’t I A Woman?