Using Case Studies to Teach Responsible AI to Industry Practitioners
Project Overview
The document explores the integration of generative AI in education, particularly through a collaborative initiative between New York University and Meta to teach Responsible AI (RAI) to industry practitioners using interactive case studies. This educational model prioritizes stakeholder engagement, allowing participants to better grasp RAI principles and their practical applications. The use of real-world scenarios in case studies has proven effective in enhancing participant understanding and motivation to implement RAI concepts in their professional environments. Despite facing challenges like limited access to proprietary data and issues with participant retention, feedback has been largely positive, indicating that the interactive nature of the case studies fosters greater engagement and a deeper commitment to responsible AI practices. Overall, the findings suggest that generative AI can significantly enrich educational approaches in RAI, promoting a more informed and responsible application of AI technologies in various industries.
Key Applications
Workshops on Responsible AI using interactive case studies
Context: Industry practitioners at Meta, including legal, managerial, and technical professionals
Implementation: Workshops designed with a stakeholder-first approach, using case studies relevant to participants' work
Outcomes: Participants reported an improved understanding of RAI principles and motivation to apply them in their work
Challenges: Limited access to proprietary information and participant attrition
Implementation Barriers
Access Barrier
Lack of access to information about the company’s proprietary algorithms and systems during case study development.
Proposed Solutions: Select case studies with publicly available information relevant to the industry.
Engagement Barrier
Organizational changes at Meta led to team restructuring, complicating efforts to achieve collective buy-in.
Proposed Solutions: Foster strong partnerships and maintain communication with organizational representatives.
Participation Barrier
High attrition rates in workshop sessions, with significant drop-off by the final session.
Proposed Solutions: Condense workshop sessions to reduce time commitment and improve attendance.
Project Team
Julia Stoyanovich
Researcher
Rodrigo Kreis de Paula
Researcher
Armanda Lewis
Researcher
Chloe Zheng
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Julia Stoyanovich, Rodrigo Kreis de Paula, Armanda Lewis, Chloe Zheng
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai