Learning goals

After completing this assignment, students should be able to:

  • Explain the concept of predictive policing and give an example of how it has been used
  • Explain how criminal recidivism scores might be racially biased
  • Explain why and how a variable like zip code can act as a proxy for race in a predictive algorithm

Content

  • Read:
    • Lum and Isaac (2016)
    • O’Neil (2016), Ch. 5 - Civilian Casualties
    • D’Ignazio and Klein (2020), Ch. 2 - Collect, Analyze, Imagine, Teach
    • Angwin et al. (2016)
  • Supplementary reading (optional):

In-class activity

Teach the rest of the class:

  1. How did Lum and Isaac demonstrate that the use of the ProdPol predictive policing algorithm would have resulted in bias by the Oakland Police Department? What data sources did they use? What is selection bias? What is confirmation bias?

  2. How does Northpointe’s COMPAS algorithm assess risk? What factors does it include? How does race factor into the calculation? Is the algorithm a “black box?” How did ProPublica conduct its investigation? Where did it find the data?

  3. Consider Table 2.1 from DF. Why do the authors argue for the set of terms on the right? What is co-liberation? What is the difference between equality and equity?

  4. What is The Man Factory? What role does it play in STEM Education? Is Smith College part of the Man Factory? How would you explain these concepts to, say, the Smith College Board of Trustees?

Assignment

Respond to the following prompts in a single R Markdown document (.Rmd). Use section headers (#) to separate one response from another.

  • In one paragraph, explain how algorithms for criminal recidivism or predictive policing could be racially biased, even when they don’t include race as an input variable.

  • In one or two paragraphs, briefly weigh the pros and cons of using algorithms like COMPAS and PredPol for criminal justice. What are the benefits? What are the dangers?

  • In one paragraph, reflect on your own feelings about algorithms in the criminal justice space in light of your response to the previous question.

Submission

Render your R Markdown document as HTML. Submit your HTML file to Moodle by Sunday at 11:59 pm ET.

Rubric

Ethics codes rubric
Criteria Zero One
Overall Quality Incomplete or missing submission. Student did not make an earnest effort to complete the assignment. Formatting errors make the submission unreadable. Student accurately summarizes the content of the documentary. Student’s reflection is thoughtful and genuine.
Angwin, Julia, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. “Machine Bias.” ProPublica; ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
D’Ignazio, Catherine, and Lauren F Klein. 2020. Data Feminism. MIT Press. https://mitpress.mit.edu/books/data-feminism.
Lum, Kristian, and William Isaac. 2016. “To Predict and Serve?” Significance 13 (5): 14–19. https://doi.org/10.1111/j.1740-9713.2016.00960.x.
O’Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown. https://www.penguinrandomhouse.com/books/241363/weapons-of-math-destruction-by-cathy-oneil/.
Rudin, Cynthia, Caroline Wang, and Beau Coker. 2020. “The Age of Secrecy and Unfairness in Recidivism Prediction.” Harvard Data Science Review 2 (1). https://doi.org/10.1162/99608f92.6ed64b30.