Responsible Machine Learning Questions

Techniques and practices to ensure machine learning systems are privacy preserving, fair, and interpretable in production. Topics include privacy preserving methods such as differential privacy and federated learning, data anonymization and utility trade offs, bias detection and mitigation strategies, fairness metrics and auditing approaches, and interpretability techniques including feature importance, feature attribution methods, local explanation techniques, and global model explanations. Also covers operationalizing these concerns in production without unacceptable performance loss, trade offs between interpretability and accuracy, governance and documentation, model auditing and provenance, and compliance with data protection regulations such as the general data protection regulation.