5.2. Unfair Discrimination and Social Bias#

Learning Outcome

Students will be able to discern bias from fairness in finance, medicine, and society in order to prevent incorrect or distorted conclusions.

Sample Tasks

  • Identify data that reflects an unwarranted bias.

  • Illustrate the reinforcement of human biases in computer models that make predictions in areas such as medical insurance, financial loans, or policing.

  • Describe the effects of biased data as it relates to red-lining.

[OhioDoHEducation21]

Our first reading, from Modern Data Science with R [BKH21], gives some examples of how seemingly neutral algorithms can produce biased results

Thinking Question

  • What other proxies for race might be present in the data?

Our second reading is Wikipedia’s entry on Redlining.

Reading Question

  • Where did the term redlining start?

  • What effects of redlining are still present now?