# The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements

arXiv: https://arxiv.org/abs/2310.06269 · HTML: https://mlsanity.com/paper/2310.06269/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 101 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 16 of 16)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022)
- [How Different Groups Prioritize Ethical Values for Responsible AI](https://mlsanity.com/paper/2205.07722/) (2022)
- [Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation](https://mlsanity.com/paper/2010.11411/) (2020)
- [Overcoming Failures of Imagination in AI Infused System Development and Deployment](https://mlsanity.com/paper/2011.13416/) (2020)
- [An Algorithmic Framework for Bias Bounties](https://mlsanity.com/paper/2201.10408/) (2022)
- [REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research](https://mlsanity.com/paper/2205.08363/) (2022)
- [Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects](https://mlsanity.com/paper/2206.06738/) (2022)
- [AI Ethics Issues in Real World: Evidence from AI Incident Database](https://mlsanity.com/paper/2206.07635/) (2022)
- [Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science](https://mlsanity.com/paper/2203.07031/) (2022)
- [Embedded EthiCS: Integrating Ethics Broadly Across Computer Science Education](https://mlsanity.com/paper/1808.05686/) (2018)
- [Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence](https://mlsanity.com/paper/2111.00826/) (2021)

## Built on by (most cited first, 8 of 8)

- [The Role of Governments in Increasing Interconnected Post-Deployment Monitoring of AI](https://mlsanity.com/paper/2410.04931/) (2024)
- [Lessons for Editors of AI Incidents from the AI Incident Database](https://mlsanity.com/paper/2409.16425/) (2024)
- [From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms](https://mlsanity.com/paper/2502.08166/) (2025)
- [AI for All: Identifying AI incidents Related to Diversity and Inclusion](https://mlsanity.com/paper/2408.01438/) (2024)
- [Automating AI Failure Tracking: Semantic Association of Reports in AI Incident Database](https://mlsanity.com/paper/2507.23669/) (2025)
- [The World of AI: A Novel Approach to AI Literacy for First-year Engineering Students](https://mlsanity.com/paper/2506.08041/) (2025)
- [Investigating In-Context Privacy Learning by Integrating User-Facing Privacy Tools into Conversational Agents](https://mlsanity.com/paper/2603.19416/) (2026)
- [Ethics Training Agents: Facilitating Group-Based Ethics Education with Role-Playing and Discussion for Ethical Reflection and Exploration](https://mlsanity.com/paper/2609.11529/) (2026)

## 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.