This paper presents Solidago, an end-to-end modular pipeline to allow any community of users to collaboratively score any number of entities. Solidago proposes a six-module decomposition. First, it uses pretrust and peer-to-peer vouches to assign trust scores to users. Second, based on participation, trust scores are turned into voting rights per user per entity. Third, for each user, a preference model is learned from the user's evaluation data. Fourth, users' models are put on a similar scale. Fifth, these models are securely aggregated. Sixth, models are post-processed to yield human-readable global scores. We also propose default implementations of the six modules, including a novel trust propagation algorithm, and adaptations of state-of-the-art scaling and aggregation solutions. Our pipeline has been successfully deployed on the open-source platform tournesol.app. We thereby lay an appealing foundation for the collaborative, effective, scalable, fair, interpretable and secure scoring of any set of entities.

翻译：本文提出Solidago，一种端到端的模块化流水线，允许任何用户社区协同对任意数量的实体进行评分。Solidago采用六模块分解结构：首先，利用预置信任与点对点担保机制为用户分配信任分数；其次，根据参与度将信任分数转化为每位用户对每个实体的投票权；第三，基于用户的评估数据学习其偏好模型；第四，将用户模型调整至统一量纲；第五，安全聚合所有用户模型；第六，对聚合模型进行后处理以生成人类可读的全局评分。我们同时提出了六个模块的默认实现方案，包括一种新颖的信任传播算法，以及对前沿量纲调整与聚合方案的适应性改进。该流水线已在开源平台tournesol.app成功部署。由此，我们为任何实体集合的协同、高效、可扩展、公平、可解释且安全的评分系统奠定了坚实基础。