提前识别AI性能与任务需求的差距，防范潜在算法危害。
AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development
- 构建七类矩阵，映射影响AI表现的关键因素
- 分析774个案例，定位高风险开发环节
- 适合在早期评估阶段预防AI失败的团队使用
AI系统常因期望过高而未能兑现承诺，导致未预见的伤害和错失收益机会。我们观察到大量‘AI不匹配’现象，即系统实际性能低于保障安全与共同创造价值所需水平。此类问题在开发中后期尤为棘手，凸显早期干预的重要性。面对复杂多维的风险因素，本文提出一种AI不匹配方法，聚焦现实模型性能与任务需求之间的差距。通过对774个案例的分析，提炼出一组关键因素，并据此构建七类矩阵，揭示因素间的关联并标示高风险区域。案例研究显示，该方法可有效降低开发过程中的风险。
原文摘要 · Abstract (English)
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.
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