The document discusses responsible and useful machine intelligence by focusing on model fidelity, data quality, fairness, and explainability. It emphasizes the importance of evaluating whether models are interpretable and perform well on live data, learning the right features from representative data, providing sensible predictions, and generating explanations that humans can understand. The key themes are ensuring models are useful, fair, robust to changes over time, and their behavior can be justified and monitored once deployed.