如果你是搞生成模型评测的，这篇论文指出了公平性评估里常被忽略的问题：该拿什么目标分布去比。它给了个框架，还附了AP-Bench的具体数值。
这篇论文关注开放生成任务中的公平性评估，指出当模型生成“美国CEO”时，缺乏明确的人口统计目标分布。作者将目标构建拆解为评估对象、先验可采纳性、分配和操作化四个承诺，并在地理成员解释下允许地理先验。在AP-Bench基准上，地理派生目标与生成结果存在0.508至0.606的分布差异；改用等类别对照后，模型特定的JSD2变化范围达0.279至0.355。论文表明目标构建本身是公平性评估的组成部分，而非评估前的附属步骤。
Who Should Be Generated? Justifying Demographic Targets in Open-Ended Generation
Fairness evaluation concerns not only what a model produces, but also what its outputs ought to be compared against. When a model generates "a CEO in the United States," the prompt leaves demographic realization to the model. Existing group fairness definitions assume that sensitive attributes are given on the input side. Generative audits instead examine output-side demographic composition, yet the targets they compare it against are typically supplied rather than justified. The upstream question is what the target distribution should be. We formalize this missing-target problem for demographic-value-unspecified generation and decompose target construction into four commitments: the evaluative object, prior admissibility, allocation, and operationalization. In this framework, we admit the geographic prior under a geographic-membership interpretation for the declared public-world use. The occupational prior, under an incumbency interpretation, requires an independently defended objective such as workforce-composition fidelity. Instantiating this construction in AP-Bench, we find substantial distribution divergence from geography-derived targets, ranging from 0.508 to 0.606 on a 0-to-1 scale. Replacing each geography-derived target with an equal-category comparator, while holding generations and measurement fixed, produces model-specific mean absolute cell-level $\mathrm{JSD}_2$ changes ranging from 0.279 to 0.355. Target construction is therefore not a preliminary to fairness evaluation but a component of it. What we supply is not a universal target, but a framework that makes explicit the justification required before a distribution can serve as a fairness standard.