The demands for accurate and representative generative AI systems means there is an increased demand on participatory evaluation structures. While these participatory structures are paramount to to ensure non-dominant values, knowledge and material culture are also reflected in AI models and the media they generate, we argue that dominant structures of community participation in AI development and evaluation are not explicit enough about the benefits and harms that members of socially marginalized groups may experience as a result of their participation. Without explicit interrogation of these benefits by AI developers, as a community we may remain blind to the immensity of systemic change that is needed as well. To support this provocation, we present a speculative case study, developed from our own collective experiences as AI researchers. We use this speculative context to itemize the barriers that need to be overcome in order for the proposed benefits to marginalized communities to be realized, and harms mitigated.

翻译：对准确且具代表性的生成式人工智能系统的需求，意味着对参与式评估架构的需求日益增长。尽管这些参与式架构对于确保非主导性价值观、知识及物质文化同样能在人工智能模型及其生成内容中得到体现至关重要，但我们认为，当前人工智能开发与评估中主流的社群参与架构，并未充分阐明社会边缘群体成员可能因其参与而获得的收益或遭受的损害。若人工智能开发者不对此类收益进行明确审视，作为学术共同体，我们亦可能对所需系统性变革的艰巨性视而不见。为支撑这一论点，我们基于自身作为人工智能研究者的集体经验，提出一个思辨性案例研究。在此思辨情境中，我们系统列举了为实现对边缘社群所承诺的收益并减轻其潜在损害而必须克服的障碍。