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Using Case Studies to Teach Responsible AI to Industry Practitioners

Julia Stoyanovich, Rodrigo Kreis de Paula, Armanda Lewis, Chloe Zheng

TL;DR

Responsible AI (RAI) education for industry practitioners is under-resourced, prompting a stakeholder-first, case-study based approach co-developed with Meta. The authors implement a two-session, interactive workshop design, supported by case study documentation, stakeholder matrices, and dual-framing handouts, culminating in practitioners acting as case study creators. Across housing ads delivery and toxic content moderation scenarios, participants reported high engagement, improved understanding of RAI concepts, and motivation to apply what they learned, despite some attrition and internal data-access challenges. The work offers a practical, scalable framework for embedding RAI principles into organizational processes and informs future industry–academic collaborations and resource sharing.

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.

Using Case Studies to Teach Responsible AI to Industry Practitioners

TL;DR

Responsible AI (RAI) education for industry practitioners is under-resourced, prompting a stakeholder-first, case-study based approach co-developed with Meta. The authors implement a two-session, interactive workshop design, supported by case study documentation, stakeholder matrices, and dual-framing handouts, culminating in practitioners acting as case study creators. Across housing ads delivery and toxic content moderation scenarios, participants reported high engagement, improved understanding of RAI concepts, and motivation to apply what they learned, despite some attrition and internal data-access challenges. The work offers a practical, scalable framework for embedding RAI principles into organizational processes and informs future industry–academic collaborations and resource sharing.

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.
Paper Structure (26 sections, 3 figures, 1 table)

This paper contains 26 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Engaging the Organizational Partner
  4. Case Studies for Teaching RAI
  5. Case study selection.
  6. Case study documentation.
  7. Stakeholder matrices.
  8. Handouts.
  9. Workshop implementation
  10. Learning outcomes
  11. Workshop schedule.
  12. Selected case studies.
  13. Practitioners as case study creators and assessors.
  14. Analysis of Engagement and Learning
  15. What was successful and what were some challenges?
  16. ...and 11 more sections

Figures (3)

  • Figure 1: Housing ads case study: negative sentiment hand-out. Yellow highlights emphasize stakeholders, and red highlights emphasize the negative sentiment. Highlights were not part of the handout used during the workshops. See Supplementary Materials for a complete set of handouts.
  • Figure 2: Jamboards for the housing ads case study: benefits, harms, and mitigation strategies.
  • Figure 3: Self-assessment of RAI knowledge: pre-workshop responses from all participants ($n=38)$ in light blue, pre-workshop responses from participants who also submitted post-workshop surveys ($n=16$) in dark blue, and post-workshop responses from the same participants in orange.