Exploring Visualization for Fairness in AI Education

AI systems are becoming omnipresent in our daily lives, but they can sometimes be a source of bias for disadvantaged groups. Lack of fairness in AI systems is not just an engineering issue that influences public policy, it also has important implications for business ethics and corporate social responsibility. To educate nontechnical students at the business school, we have developed educational modules on fairness in AI that convey the importance of making not just accurate but also equitable business decisions. We introduce an educational module with six interactive components that illustrate how to detect, quantify, and mitigate biases in a logistic regression model. When such a module was deployed in a "Fair Algorithms for Business" course, it was shown to increase students' engagement and understanding. We further conducted a user study with 413 participants to examine whether adding visualizations and interactions (or not) could lead to an increased understanding of fairness concepts.