Title: Discussion
Course: Advanced Data Science: A Graduate Course > Section: Module 7: Data and AI ethics and governance

Consider a concrete deployment the instructor names each term (for example a pretrial risk score, a loan-approval model, or a hospital readmission flag). Which fairness definition should this deployment prioritize, calibration, equalized odds, or demographic parity, and why does the context justify that choice over the alternatives? Post your initial position by midweek, naming the harm you are most trying to prevent and the tradeoff you accept. Then reply substantively to two peers who chose a different definition, engaging their reasoning rather than restating yours. The instructor facilitates each thread, pressing on unexamined assumptions and surfacing the value judgments hiding inside apparently technical claims.