Graduate Teaching Assistant — ML Fairness and Responsible AI (RPI)
Developed and graded ML fairness audit assignments focused on evaluating bias, calibration, and explanation quality in responsible AI coursework. Supported counterfactual fairness auditing using SHAP-based and DiCE-style explanation workflows and provided detailed written feedback on student audit projects and AI ethics case analyses. Helped expand course materials with new lab exercises for end-to-end responsible AI audit capstones using real credit-scoring datasets. • Designed fairness audit assignments and recitations for CSCI 4965 (Machine Learning) and CSCI 6961 (Responsible AI: Fairness, Accountability, and Transparency). • Instructed students on disparate impact, equalized odds calibration, SHAP global/local explanations, and counterfactual generation. • Implemented/maintained guided capstone workflows and assessment rubrics for credit scoring auditing. • Used Python plus IBM AI Fairness 360, SHAP, and Fairlearn tooling to structure evaluation and student feedback.