#OpenBox - Charting the Sociotechnical Gap in Explainable AI - Part 2
OPENBOX aims to bring an easier understanding of open problems that helps in finding solutions for such problems. Today, we have with us Upol Ehsan is a Researcher and Doctoral Candidate in the School of Interactive Computing at Georgia Tech and an affiliate at the Data & Society Research Institute. Combining AI, HCI, and philosophy, his work in Explainable AI (XAI) and Responsible AI aims to foster a future where anyone, regardless of their background, can use AI-powered technology with dignity. His work has pioneered the area of Human-centered Explainable AI (a sub-field of XAI), receiving multiple awards at ACM CHI, FAccT, and HCII and been covered in major media outlets. By promoting equity and ethics in AI, he wants to ensure stakeholders who aren’t at the table do not end up on the menu. Outside research, he is a founder and advisor for Aalor Asha, an educational institute he started for underprivileged children subjected to child labor. He is also a social entrepreneur and has co-founded DeshLabs, a social innovation lab focused on fostering grassroots innovations in emerging markets. We discuss the paper titled “Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI” which It will be presented at CSCW 2023 and was co-authors with Koustuv Saha, Munmun de Choudhury, and Mark Riedl. Upol explains about specific nuances on why Explainability cannot be considered independent of model development and deployment environment. This is part 2 of the discussion. --- Send in a voice message: https://podcasters.spotify.com/pod/show/ryan-carrier3/message