提出公式评估多AI筛选的精度，指导如何用多样性提升决策可靠性。
Quantifying how AI Panels improve precision
- 用数学公式建模多AI协同筛选的精度上限，考虑相关性与数量影响。
- 公式显示：当AI相关性低、数量多时，选中优质候选人的精度显著提升。
- 适合关注公平选拔、避免单一AI风险的政策制定者和系统设计者。
人工智能在求职筛选等应用中日益普及，可能加剧青年失业问题。即使没有偏见，依赖单一AI也存在风险。本文推导出一个公式来估算类似真实简历数据下多AI面板的精度上限：$P(q) \approx \frac{ρn^b + q(1-ρ)}{1 + (n^b - 1)ρ}$，其中 $b \approx q^* + 0.8 (1 - ρ)$，$q^*$ 为 $q$ 截断至 $[0.07, 0.22]$ 区间，$P(q)$ 表示由 $n$ 个AI组成的面板选出前 $q$ 分位数的精度，$ρ$ 为平均成对相关性。该公式可用于决定关键决策中应使用多少个AI。通过量化分析多AI多样性带来的优势，可减少对单一AI系统的依赖，推动在重要社会经济系统中构建更具弹性的AI结构。
原文摘要 · Abstract (English)
AI in applications like screening job applicants had become widespread, and may contribute to unemployment especially among the young. Biases in the AIs may become baked into the job selection process, but even in their absence, reliance on a single AI is problematic. In this paper we derive a simple formula to estimate, or at least place an upper bound on, the precision of such approaches for data resembling realistic CVs: $P(q) \approx \frac{ρn^b + q(1-ρ)}{1 + (n^b - 1)ρ}$ where $b \approx q^* + 0.8 (1 - ρ)$ and $q^*$ is $q$ clipped to $[0.07, 0.22]$ where $P(q)$ is the precision of the top $q$ quantile selected by a panel of $n$ AIs and $ρ$ is their average pairwise correlation. This equation provides a basis for considering how many AIs should be used in a Panel, depending on the importance of the decision. A quantitative discussion of the merits of using a diverse panel of AIs to support decision-making in such areas will move away from dangerous reliance on single AI systems and encourage a balanced assessment of the extent to which diversity needs to be built into the AI parts of the socioeconomic systems that are so important for our future.
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