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.

翻译：在筛选求职者等应用中，AI已变得广泛，并可能导致失业，尤其在年轻人中。AI的偏见可能固化于职位选择过程，但即使没有偏见，依赖单一AI也存在问题。本文推导出一个简单公式，用于估算（或至少设定上限）此类方法在接近真实简历数据上的精度：$P(q) \approx \frac{ρn^b + q(1-ρ)}{1 + (n^b - 1)ρ}$，其中$b \approx q^* + 0.8 (1 - ρ)$，$q^*$为$q$截断至[0.07, 0.22]后的值，$P(q)$表示由$n$个AI组成的小组所选顶部$q$分位数的精度，$ρ$为其平均 pairwise相关系数。该公式为根据决策重要性考虑小组中应使用多少AI提供了基础。关于在支持决策的此类领域中使用多样化AI小组优点的定量讨论，将有助于摆脱对单一AI系统的危险依赖，并鼓励平衡评估在对于未来至关重要的社会经济系统的AI部分中，多样性需要达到何种程度。