Responsible AI (RAI) practices are increasingly important for practitioners in anticipating and addressing potential harms of AI, and emerging research suggests that AI practitioners often learn about RAI on-the-job. More generally, learning at work is social; thus, this work explores the interpersonal aspects of learning about RAI on-the-job. Through workshops with 21 industry-based RAI educators, we offer the first empirical investigation into interpersonal processes and dimensions of learning about RAI at work. This study finds key phases of RAI are sites for ongoing interpersonal learning, such as critical reflection about potential RAI impacts and collective sense-making about operationalizing RAI principles. We uncover a significant gap between these interpersonal learning processes and current approaches to learning about RAI. Finally, we identify barriers and supports for interpersonal learning about RAI. We close by discussing opportunities to better enable interpersonal learning about RAI on-the-job and the broader implications of interpersonal learning for RAI.
https://dl.acm.org/doi/10.1145/3706598.3714144
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)