# Using Case Studies to Teach Responsible AI to Industry Practitioners

arXiv: https://arxiv.org/abs/2407.14686 · HTML: https://mlsanity.com/paper/2407.14686/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2024.

## Built on by (most cited first, 3 of 3)

- [Framework, Standards, Applications and Best practices of Responsible AI : A Comprehensive Survey](https://mlsanity.com/paper/2504.13979/) (2025)
- [VirnyFlow: Optimizing ML Pipelines for Accuracy, Fairness, and Stability at Scale](https://mlsanity.com/paper/2506.01584/) (2025)
- [Design Concept: Scaffolding Geopolitical Reflection Among Tech Workers](https://mlsanity.com/paper/2607.28904/) (2026)

## Abstract (arXiv)

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.