How do you think about fairness and ethics in machine learning?

Ethical ML considers harm, bias, and transparency. Practices: - Evaluate performance across groups - Audit training data for bias - Use fairness constraints when needed - Document assumptions and limitations Also consider privacy, consent, and the consequences of errors. Communicate trade-offs clearly to stakeholders.