Objective: Firearm injury research necessitates using data from often-exploited vulnerable populations of Black and Brown Americans. In order to minimize distrust, this study provides a framework for establishing AI trust and transparency with the general population. Methods: We propose a Model Facts template that is easily extendable and decomposes accuracy and demographics into standardized and minimally complex values. This framework allows general users to assess the validity and biases of a model without diving into technical model documentation. Examples: We apply the Model Facts template on two previously published models, a violence risk identification model and a suicide risk prediction model. We demonstrate the ease of accessing the appropriate information when the data is structured appropriately. Discussion: The Model Facts template is limited in its current form to human based data and biases. Like nutrition facts, it also will require some educational resources for users to grasp its full utility. Human computer interaction experiments should be conducted to ensure that the interaction between user interface and model interface is as desired. Conclusion: The Model Facts label is the first framework dedicated to establishing trust with end users and general population consumers. Implementation of Model Facts into firearm injury research will provide public health practitioners and those impacted by firearm injury greater faith in the tools the research provides.

翻译：目标：枪支伤害研究需要使用来自常被剥削的黑人和棕色人种美国弱势群体的数据。为减少不信任，本研究提出一个框架，旨在与普通大众建立人工智能信任与透明度。方法：我们提出一个易于扩展的“模型事实”模板，将准确性和人口统计数据分解为标准化且最低复杂度值。该框架允许普通用户无需深入技术模型文档即可评估模型的有效性和偏见。实例：我们将“模型事实”模板应用于两个先前发布的模型——暴力风险识别模型和自杀风险预测模型，并展示了当数据适当结构化时，获取适当信息的便捷性。讨论：“模型事实”模板目前仅适用于基于人类的数据和偏见。与营养事实类似，它也需要一定的教育资源帮助用户充分掌握其效用。应开展人机交互实验，确保用户界面与模型界面之间的交互达到预期效果。结论：“模型事实”标签是首个致力于与终端用户和普通大众建立信任的框架。将“模型事实”应用于枪支伤害研究，将使公共卫生从业者及受枪支伤害影响的人对研究提供的工具更加信任。