From the course: Foundations of Responsible AI
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Learning from real-world RAI implementations
From the course: Foundations of Responsible AI
Learning from real-world RAI implementations
- Implementing responsible AI can look very different depending on the sector, the team, even the specific use case. But across organizations that are doing this work well, there are patterns, practices that tend to show up consistently even when the systems themselves are complex or fast moving. This video focuses on what we can learn from real-world implementations. One pattern is early integration. Teams that succeed with responsible AI build it into the development process from the very beginning. It might include dedicated review points across model planning, defined standards for data documentation, or shared metrics for fairness and performance. What matters isn't where the work happens, but when. When responsibility shows up early, it tends to stick. Another is shared responsibility. High functioning teams don't rely on a single expert or assign one individual to carry responsibility. They distribute ownership across engineering, product, compliance, and legal teams. Each…
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