From the Field: Operational RAI Insights
This page is where I post reflections based on my personal experiences in the RAI space. These include patterns I’ve observed that tend to help Responsible AI efforts succeed, common pitfalls I’ve seen teams struggle with, and practical challenges organizations face when trying to operationalize AI ethics at scale.
The “Socio” in Sociotechnical: Why Human Impact Must Stay at the Center of RAI
Imagine you’re observing a Responsible AI team deeply focused on a product or model review. For this particular session, everything looks great: policies are codified, confidence scores are acceptable, severity levels are within threshold, and checklists are complete. The review is a success from a process standpoint. Everything seems to work exactly as designed, and so the product launches.
But somewhere in the real world, a darker-skinned user gets misidentified by a vision model, a woman’s resume is not flagged as qualified for a role that she's well suited to fill, or a non-English speaker gets lower quality results than native English speakers. The system may have passed the evaluations, but it still failed someone.