AI2030 Responsible AI Tools Marketplace

Key Takeaways

AI harms are real and widespread: The study found that when students interacted with the AI Incident Database, they gained a better understanding of "the magnitude and severity of AI harms," suggesting that concrete examples of AI failures in real-world deployments are essential for raising awareness about the practical risks of AI systems.
There's a critical gap between technical and ethical training: Students expressed "a desire to close the educational gap between their technical skills and their ability to think systematically about ethical and societal aspects of their work," indicating that computer science curricula often prioritize technical competency over the ethical considerations necessary for responsible AI development.
Concrete incident databases can motivate responsible practices: Exposure to documented AI incidents through AIID instilled in students "a sense of urgency around (a) designing functional and safe AI and (b) strengthening governance and accountability mechanisms," demonstrating that learning about real harms can drive motivation to build safer systems and support stronger oversight.
Educational tools need continuous improvement: The paper compiles "actionable recommendations" based on student feedback about AIID's limitations, suggesting that while incident databases are valuable for AI ethics education, they require ongoing refinement to be maximally effective as teaching resources.
AI ethics education should be a core curriculum requirement: The study provides evidence that "one of the critical objectives of AI Ethics education must be to raise awareness of AI harms," supporting the case for making ethics training a standard part of computer science and data science programs.

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.

37 pages, 11 figures; To appear in the proceedings of EAAMO 2023

Key Takeaways

AI harms are real and widespread: The study found that when students interacted with the AI Incident Database, they gained a better understanding of "the magnitude and severity of AI harms," suggesting that concrete examples of AI failures in real-world deployments are essential for raising awareness about the practical risks of AI systems.
There's a critical gap between technical and ethical training: Students expressed "a desire to close the educational gap between their technical skills and their ability to think systematically about ethical and societal aspects of their work," indicating that computer science curricula often prioritize technical competency over the ethical considerations necessary for responsible AI development.
Concrete incident databases can motivate responsible practices: Exposure to documented AI incidents through AIID instilled in students "a sense of urgency around (a) designing functional and safe AI and (b) strengthening governance and accountability mechanisms," demonstrating that learning about real harms can drive motivation to build safer systems and support stronger oversight.
Educational tools need continuous improvement: The paper compiles "actionable recommendations" based on student feedback about AIID's limitations, suggesting that while incident databases are valuable for AI ethics education, they require ongoing refinement to be maximally effective as teaching resources.
AI ethics education should be a core curriculum requirement: The study provides evidence that "one of the critical objectives of AI Ethics education must be to raise awareness of AI harms," supporting the case for making ethics training a standard part of computer science and data science programs.

Abstract

37 pages, 11 figures; To appear in the proceedings of EAAMO 2023

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

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

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

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links