
Hosted by Roland Brown · EN

The episode explores the concept of responsible AI and the role of humans in AI systems. It discusses the seduction of automation, the danger of full automation, effective human in the loop design, and common anti-patterns in AI systems. The importance of context, trust, and governance in AI systems is emphasized, highlighting the need for operational governance of AI as a product.TakeawaysHuman in the loopResponsible AI🎧 Listen to The Data Journey wherever you get your podcasts, or visit thedatajourney.com

The podcast episode explores the distinction between training data and inference data, highlighting the architectural discipline required for each type of data. It emphasises the challenges and root causes of architectural issues, and introduces the three-layer model for trust and the importance of metadata and lineage for AI systems.TakeawaysTraining data and inference data require different architectural discipline and trust guarantees.Metadata and lineage are crucial for AI systems, more so than for analytics.🎧 Listen to The Data Journey wherever you get your podcasts, or visit thedatajourney.com