Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges

要旨

Technology companies continue to invest in efforts to incorporate responsibility in their Artificial Intelligence (AI) advancements, while efforts to audit and regulate AI systems expand. This shift towards Responsible AI (RAI) in the tech industry necessitates new practices and adaptations to roles—undertaken by a variety of practitioners in more or less formal positions, many of whom focus on the user-centered aspects of AI. To better understand practices at the intersection of user experience (UX) and RAI, we conducted an interview study with industrial UX practitioners and RAI subject matter experts, both of whom are actively involved in addressing RAI concerns throughout the early design and development of new AI-based prototypes, demos, and products, at a large technology company. Many of the specifc practices and their associated challenges have yet to be surfaced in the literature, and distilling them offers a critical view into how practitioners’ roles are adapting to meet present-day RAI challenges. We present and discuss three emerging practices in which RAI is being enacted and reifed in UX practitioners’ everyday work. We conclude by arguing that the emerging practices, goals, and types of expertise that surfaced in our study point to an evolution in praxis, with associated challenges that suggest important areas for further research in HCI.

著者
Qiaosi Wang
Georgia Institute of Technology, Atlanta, Georgia, United States
Michael Madaio
Google Research, New York, New York, United States
Shaun Kane
Google Research, Boulder, Colorado, United States
Shivani Kapania
Google Research, Bengaluru, India
Michael Terry
Google Research, Cambridge, Massachusetts, United States
Lauren Wilcox
Google Research, Mountain VIew, California, United States
論文URL

https://doi.org/10.1145/3544548.3581278

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Explainable, Responsible, Manageable AI

Hall D
6 件の発表
2023-04-26 18:00:00
2023-04-26 19:30:00