Human-Centered AI (HCAI) is an emerging discipline that aims to create AI systems that amplify and augment human abilities and preserve human control in order to make AI partnerships more productive, enjoyable, and fair. Our workshop aims to bring together researchers and practitioners from the NeurIPS and HCI communities and others with convergent interests in HCAI. With an emphasis on diversity and discussion, we will explore research questions that stem from the increasingly wide-spread usage of machine learning algorithms across all areas of society, with a specific focus on understanding both technical and design requirements for HCAI systems, as well as how to evaluate the efficacy and effects of HCAI systems.
Cecilia Aragon, University of Washington, US. Dr. Aragon founded and directs the Human Centered Data Science Lab at University of Washington. Her research focuses on enabling humans to gain insights from
large datasets through a combination of machine learning and qualitative, quantitative, and visualization analyses. Dr. Aragon’s book, Human Centered Data Science, will be published by MIT Press in 2022.
Barbara Poblete, University of Chile; Millenium Institute on Data, Chile; Amazon. Dr. Poblete co-directs the “Fake News and Misinformation” multidisciplinary research group at the Millenium Institute on Data. Her research areas are Social Network Analysis, Web Data Mining, Crisis Informatics and Applied Machine Learning. Her work “Information Credibility on Twitter” was awarded the 2021 Seoul Test of Time Award by the IW3C2 at The Web Conference.
Wendy Mackay, Inria; Université Paris-Saclay, France. Dr. Mackay directs the ExSitu research group in HCI at Inria and Université Paris-Saclay. Through study of users who push the limits of interaction and their use patterns regarding complex phenomena, Dr. Mackay explores the future of interactive technologies for creative professionals, with a particular focus on human-AI interaction and collaboration. She is an ACM Fellow and the 2021-22 Computer Science Chair for the Collège de France.
Cynthia Rudin, Duke University, US. Dr. Rudin’s research focuses on machine learning tools that help humans make better decisions, mainly interpretable machine learning and interpretable deep learning with domain-based constraints. She applies these methods to critical societal problems in criminology, healthcare, and energy grid reliability, as well as to computer vision.
WHEN AND WHERE
Monday 13 December 2021, online
Submissions are now closed.