AI대학원 콜로퀴엄(Online) 내일 4/9 AI와 Fairness 주제 강연, 이민경교수(Univ. of Texas, Austin)

내일(4/9) Human-centered AI 분야 연구를 활발하게 하고 계신 Univ. of Texas at Austin 이민경 교수님을 초청하여 

최근 화두가 되고 있는 AI와 Fairness에 대한 강연을 듣습니다. 관심있는 분들의 많은 참석 바랍니다!

본 강연은 영어로 진행됩니다. (The Lecture will be in English)

 

----- 2021년 4월 9일(금) 강연 정보 -----

연사: 이민경 (Min Kyung Lee), University of Texas at Austin 교수

제목: 공정하고 참여적인 인공지능 디자인을 위한 연구
(Enabling participatory and procedurally-fair AI)

일시: 4/9(금) 오전 10:00-11:10 (Zoom)

[사전 등록] https://bit.ly/PNUAI-Colloquium  (사전 등록 시 Zoom URL을 제공합니다)

 

강연 요약: 

As artificial intelligence (AI) is transforming work and society, it is ever more important to ensure that AI systems are fair and trustworthy and support critical values and priorities in organizations and communities. In this talk, I will first present empirical findings on people’s trust and fairness around algorithms that make managerial and resource allocation decisions. My research suggests that techniques for distributive fairness are not sufficient for gaining people’s trust in AI. Addressing this gap, I propose two Frameworks for achieving procedurally-fair and participatory AI: a procedural justice Framework that lays out considerations for procedural fairness in algorithmic decisions, and a participatory Framework called WeBuildAI that enables people to build algorithms for their own communities. I present a case study of this Framework with a nonprofit called 412 Food Rescue in which stakeholders used the Framework to build a food donation matching algorithm and adjudicate equity and efficiency trade-offs in the algorithm.

연사 소개:

Min Kyung Lee is an assistant professor in the School of Information at the University of Texas at Austin. Dr. Lee is a human-computer interaction researcher, and has extensive experiences in developing theories, methods and tools for human-centered AI and deploying them in practice through collaboration with real-world stakeholders and organizations. She proposed a Participatory Framework that empowers community members to design matching algorithms that govern their own communities. She also conducted one of the first studies investigating public perceptions of algorithmic fairness and the impacts of algorithmic management. Her current research is inspired by and complements her previous work on social robots for long-term interaction, seamless human-robot handovers, and telepresence robots.

Dr. Lee is a Siebel Scholar and has received the Allen Newell Award for Research Excellence, research grants from NSF and Uptake, and five best paper awards and honorable mentions and two demo/video awards in venues such as CHI, CSCW, DIS and HRI. She is an associate editor of ACM Transactions on Human-Robot Interaction. Her work has been featured in media outlets such as the New York Times, New Scientist, Washington Post, MIT Technology Review and CBS. Prior to UT Austin, she was a research scientist at Carnegie Mellon University. She received a PhD and a MS in Human-Computer Interaction and an MDes in Interaction Design from Carnegie Mellon University and a BS from KAIST. Website:  http://minlee.net