How can I analyze error patterns for possible fairness issues?

Example Concept: Fairness evaluation involves comparing model error rates across different demographic groups to identify disparities. By using fairness metrics like disparate impact, equal opportunity, or demographic parity, you can assess whether the model's errors are equitably distributed. Tools like fairness dashboards can visualize these metrics, allowing for a clearer understanding of potential biases.