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

Authors:

Michael Feffer,

Nikolas Martelaro,

Hoda HeidariAuthors Info & Claims

EAAMO '23: Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization

Article No.: 3, Pages 1 - 11

https://doi.org/10.1145/3617694.3623223

Published: 30 October 2023 Publication History

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Abstract

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.

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Cited By

Bogucka EConstantinides MDe Miguel Velazquez JScepanovic SQuercia DGvirtz A(2024)The Atlas of AI Incidents in Mobile Computing: Visualizing the Risks and Benefits of AI Gone MobileAdjunct Proceedings of the 26th International Conference on Mobile Human-Computer Interaction10.1145/3640471.3680447(1-6)Online publication date: 21-Sep-2024
https://dl.acm.org/doi/10.1145/3640471.3680447
Scheuerman M(2024)In the Walled Garden: Challenges and Opportunities for Research on the Practices of the AI Tech IndustryProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658918(456-466)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658918
De Miguel Velázquez JŠćepanović SGvirtz AQuercia D(2024)Decoding Real-World Artificial Intelligence IncidentsComputer10.1109/MC.2024.343249257:11(71-81)Online publication date: 15-Oct-2024
https://dl.acm.org/doi/10.1109/MC.2024.3432492
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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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EAAMO '23: Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization

October 2023

498 pages

ISBN:9798400703812

DOI:10.1145/3617694

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Cited By

Bogucka EConstantinides MDe Miguel Velazquez JScepanovic SQuercia DGvirtz A(2024)The Atlas of AI Incidents in Mobile Computing: Visualizing the Risks and Benefits of AI Gone MobileAdjunct Proceedings of the 26th International Conference on Mobile Human-Computer Interaction10.1145/3640471.3680447(1-6)Online publication date: 21-Sep-2024
https://dl.acm.org/doi/10.1145/3640471.3680447
Scheuerman M(2024)In the Walled Garden: Challenges and Opportunities for Research on the Practices of the AI Tech IndustryProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658918(456-466)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658918
De Miguel Velázquez JŠćepanović SGvirtz AQuercia D(2024)Decoding Real-World Artificial Intelligence IncidentsComputer10.1109/MC.2024.343249257:11(71-81)Online publication date: 15-Oct-2024
https://dl.acm.org/doi/10.1109/MC.2024.3432492
Ananny M(2024)Making Generative Artificial Intelligence a Public Problem. Seeing Publics and Sociotechnical Problem-Making in Three Scenes of AI FailureJavnost - The Public10.1080/13183222.2024.231900031:1(89-105)Online publication date: 28-Mar-2024
https://doi.org/10.1080/13183222.2024.2319000

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