🚨 New pre-print alert! 🚨 Excited to share “The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations” w/ the amazing team: Samir Passi,Vera Liao,Larry Chan,Ethan Lee,Michael Muller (he/him) @michaelmuller.bsky.social,Mark Riedl 🔗arxiv.org/abs/2107.13509 💡Findings at a glance... 1/n

The field of AI always seems in a rush to get somewhere, making it hard to find time to take stock. The Responsible AI Maturity Model can help orgs. do just that - pause; reflect on where they are, where they want to (+should) go, and why; and strategize about how to get there.

Intern post - come work with us as part of Mickey's team!

Y'all, I've been doing a series of posts about the RAI Maturity Model on LinkedIn linkedin.com/in/mihaelavorv… Today: building a *common language* across disciplines so you can work together.

Highlighting “what” AI systems can’t do is not enough to foster realistic mental models. It is imp to explain “why” AI systems can’t do certain things. Seamful XAI helps with that. It was a joy working with Upol Ehsan, Vera Liao, Mark Riedl, and Hal Daumé III on this CSCW paper.