Using Case Studies to Teach Responsible AI to Industry Practitioners
Julia Stoyanovich, Rodrigo Kreis de Paula, Armanda Lewis, Chloe Zheng
Abstract
Responsible AI (RAI) encompasses the science and practice of ensuring that AI design, development, and use are socially sustainable--—maximizing the benefits of technology while mitigating its risks. Industry practitioners play a crucial role in achieving the objectives of RAI, yet there is a persistent a shortage of consolidated educational resources and effective methods for teaching RAI to practitioners. In this paper, we present a stakeholder-first educational approach using interactive case studies to foster organizational and practitioner-level engagement and enhance learning about RAI. We detail our partnership with Meta, a global technology company, to co-develop and deliver RAI workshops to a diverse company audience. Assessment results show that participants found the workshops engaging and reported an improved understanding of RAI principles, along with increased motivation to apply them in their work.
BibTeX
@article{Stoyanovich_Kreis de Paula_Lewis_Zheng_2025, title={Using Case Studies to Teach Responsible AI to Industry Practitioners}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35177}, DOI={10.1609/aaai.v39i28.35177}, abstractNote={Responsible AI (RAI) encompasses the science and practice of ensuring that AI design, development, and use are socially sustainable--—maximizing the benefits of technology while mitigating its risks. Industry practitioners play a crucial role in achieving the objectives of RAI, yet there is a persistent a shortage of consolidated educational resources and effective methods for teaching RAI to practitioners. In this paper, we present a stakeholder-first educational approach using interactive case studies to foster organizational and practitioner-level engagement and enhance learning about RAI. We detail our partnership with Meta, a global technology company, to co-develop and deliver RAI workshops to a diverse company audience. Assessment results show that participants found the workshops engaging and reported an improved understanding of RAI principles, along with increased motivation to apply them in their work.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Stoyanovich, Julia and Kreis de Paula, Rodrigo and Lewis, Armanda and Zheng, Chloe}, year={2025}, month={Apr.}, pages={29062-29069} }