AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development

要旨

AI systems are often introduced with high expectations, yet many fail to deliver, resulting in unintended harm and missed opportunities for benefit. We frequently observe significant "AI Mismatches", where the system’s actual performance falls short of what is needed to ensure safety and co-create value. These mismatches are particularly difficult to address once development is underway, highlighting the need for early-stage intervention. Navigating complex, multi-dimensional risk factors that contribute to AI Mismatches is a persistent challenge. To address it, we propose an AI Mismatch approach to anticipate and mitigate risks early on, focusing on the gap between realistic model performance and required task performance. Through an analysis of 774 AI cases, we extracted a set of critical factors, which informed the development of seven matrices that map the relationships between these factors and highlight high-risk areas. Through case studies, we demonstrate how our approach can help reduce risks in AI development.

著者
Devansh Saxena
University of Wisconsin-Madison, Madison, Wisconsin, United States
Ji-Youn Jung
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Jodi Forlizzi
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Kenneth Holstein
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
John Zimmerman
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
DOI

10.1145/3706598.3714098

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714098

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: AI Ethics and Concerns

G314+G315
7 件の発表
2025-04-30 01:20:00
2025-04-30 02:50:00
日本語まとめ
読み込み中…