Responsible AI: Ethics and Safety in Machine Learning
Responsible AI: Ethics and Safety in Machine Learning Responsible AI means designing, building, and deploying AI systems that respect people, protect privacy, and reduce harm. It is not only a technical issue; it is a social and legal responsibility as well. Teams that integrate ethics and safety practices gain trust and run safer, more sustainable projects. Core principles Fairness and non-discrimination: test models on diverse data, look for biased outcomes, and adjust data or models. Safety and robustness: plan for corner cases, monitor for failures, and add fail-safe mechanisms. Transparency and explainability: share how models work, their limits, and the data used. Privacy and data protection: collect only what is needed, anonymize when possible, and use privacy techniques. Accountability and governance: assign owners, keep clear records, and run audits. Human oversight: keep a human-in-the-loop for important decisions when appropriate. Practical steps for teams ...