The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements
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Abstract (arXiv)
Prior work has established the importance of integrating AI ethics topics into computer and data sciences curricula. We provide evidence suggesting that one of the critical objectives of AI Ethics education must be to raise awareness of AI harms. While there are various sources to learn about such harms, The AI Incident Database (AIID) is one of the few attempts at offering a relatively comprehensive database indexing prior instances of harms or near harms stemming from the deployment of AI technologies in the real world. This study assesses the effectiveness of AIID as an educational tool to raise awareness regarding the prevalence and severity of AI harms in socially high-stakes domains. We present findings obtained through a classroom study conducted at an R1 institution as part of a course focused on the societal and ethical considerations around AI and ML. Our qualitative findings characterize students' initial perceptions of core topics in AI ethics and their desire to close the educational gap between their technical skills and their ability to think systematically about ethical and societal aspects of their work. We find that interacting with the database helps students better understand the magnitude and severity of AI harms and instills in them a sense of urgency around (a) designing functional and safe AI and (b) strengthening governance and accountability mechanisms. Finally, we compile students' feedback about the tool and our class activity into actionable recommendations for the database development team and the broader community to improve awareness of AI harms in AI ethics education.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 16 of its 101 references that are in the map. First posted on arXiv 10 Oct 2023.
Builds on
16 of 101 references.
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4 more
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Built on by
Citing papers per year: 2024: 3; 2025: 3; 2026: 2.
8 papers.
- 2024The Role of Governments in Increasing Interconnected Post-Deployment Monitoring of AIcited by 6
- 2024Lessons for Editors of AI Incidents from the AI Incident Databasecited by 5
- 2025From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harmscited by 3
- 2024AI for All: Identifying AI incidents Related to Diversity and Inclusioncited by 2
- 2025Automating AI Failure Tracking: Semantic Association of Reports in AI Incident Databasecited by 1
- 2025The World of AI: A Novel Approach to AI Literacy for First-year Engineering Studentscited by 0
- 2026Investigating In-Context Privacy Learning by Integrating User-Facing Privacy Tools into Conversational Agentscited by 0
- 2026Ethics Training Agents: Facilitating Group-Based Ethics Education with Role-Playing and Discussion for Ethical Reflection and Explorationcited by 0
- 2026Ethics Training Agents: Facilitating Group-Based Ethics Education with Role-Playing and Discussion for Ethical Reflection and Exploration10 Sep 2026
- 2026Investigating In-Context Privacy Learning by Integrating User-Facing Privacy Tools into Conversational Agents19 Mar 2026
- 2025Automating AI Failure Tracking: Semantic Association of Reports in AI Incident Database31 Jul 2025
- 2025The World of AI: A Novel Approach to AI Literacy for First-year Engineering Students6 Jun 2025
- 2025From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms12 Feb 2025
- 2024The Role of Governments in Increasing Interconnected Post-Deployment Monitoring of AI7 Oct 2024
- 2024Lessons for Editors of AI Incidents from the AI Incident Database24 Sep 2024
- 2024AI for All: Identifying AI incidents Related to Diversity and Inclusion19 Jul 2024