Users trust algorithms more when they can predict the algorithms' behavior. Simple algorithms trivially yield predictively accurate mental models, but modern AI algorithms have often been assumed too complex for people to build predictive mental models, especially in the social media domain. In this paper, we describe conditions under which even complex algorithms can yield predictive mental models, opening up opportunities for a broader set of human-centered algorithms. We theorize that users will form an accurate predictive mental model of an algorithm's behavior if and only if the algorithm simultaneously satisfies three criteria: (1) cognitive availability of the underlying concepts being modeled, (2) concept compactness (does it form a single cognitive construct?), and (3) high alignment between the person's and algorithm's execution of the concept. We evaluate this theory through a pre-registered experiment (N=1250) where users predict behavior of 25 social media feed ranking algorithms that vary on these criteria. We find that even complex (e.g., LLM-based) algorithms enjoy accurate prediction rates when they meet all criteria, and even simple (e.g., basic term count) algorithms fail to be predictable when a single criterion fails. We also find that these criteria determine outcomes beyond prediction accuracy, such as which mental models users deploy to make their predictions.

翻译：当用户能够预测算法行为时，他们会更信任算法。简单算法显然能产生预测准确的心理模型，但现代人工智能算法通常被认为过于复杂，以至于人们无法建立预测性心理模型，尤其是在社交媒体领域。本文描述了即使复杂算法也能产生预测性心理模型的条件，这为更广泛的人本算法设计开辟了可能性。我们提出理论：当且仅当算法同时满足三个标准时，用户才能形成关于算法行为的准确预测性心理模型：(1) 被建模基础概念的认知可用性，(2) 概念紧凑性（是否形成单一认知结构？），以及(3) 个人与算法执行概念时的高度对齐性。我们通过一项预注册实验（N=1250）评估该理论，实验中用户需预测25种社交媒体信息流排序算法的行为，这些算法在这些标准上存在差异。研究发现：即使复杂算法（例如基于LLM的算法）在满足所有标准时也能获得高预测准确率；而即使简单算法（例如基础词频统计）在任一标准不满足时也会丧失可预测性。我们还发现这些标准决定了预测准确性之外的结果，例如用户采用何种心理模型进行预测。