10 Lessons Learned for Assessing and Mitigating Unexpected Patterns in AI Models (Part 1 of 3)

Getting to trustworthy AI is not simple. It takes a lot of moving parts and collective resources (well beyond a data science team) to get every element right, from people and culture to governance to data and processes. But that’s true of most worthy endeavors. In this 3 part series, author Sheri Feinzig and Phaedra Boinodiris walk you through the top 10 lessons learned for both assessing and mitigating for unexpected patterns in AI models.

Phaedra BoinodirisPhaedra BoinodirisFebruary 28, 2022lessons learned, bias, patterns, ai models, systemic empathy, AI ethics

HR and Explainable AI: A Human Being in 100,000 Dimensions

Imagine an approach to human resources whereby a human being is perceived in 100,000 dimensions. This new approach would replace the flawed traditional HR systems in place today based on limited criteria. When a human being gives us consent to use their data..

Phaedra Boinodiris, Daniel Schnelbach, Beth RuddenPhaedra BoinodirisOctober 7, 2021explainable AI, ontologies, trust, transparency, AI ethicsComment