Forecasts inform decision-making in nearly every domain. Forecasts are often
produced by experts with rare or hard to acquire skills. In practice, forecasts
are often used by domain experts and managers with little forecasting
expertise. Our study focuses on how to design forecasting software that
empowers non-expert users. We study how users can make use of state-of-the-art
forecasting methods, embed their domain knowledge, and how they build
understanding and trust towards generated forecasts. To do so, we co-designed a
forecasting software prototype using feedback from users and then analyzed
their interactions with our prototype. Our results identified three main
considerations for non-expert users: (1) a safe stepwise approach facilitating
causal understanding and trust; (2) a white box model supporting
human-reasoning-friendly components; (3) the inclusion of domain knowledge.
This paper contributes insights into how non-expert users interact with
forecasting software and by recommending ways to design more accessible
forecasting software.

我们研究了预测软件的设计，以赋予非专业用户使用最先进的预测方法，嵌入其领域知识，并建立对生成的预测的理解和信任，为非专业用户提供了三个主要考虑因素：（1）安全的逐步方法，促进因果关系的理解和信任；（2）支持人类推理友好组件的白盒模型；（3）包含领域知识。本文为非专业用户与预测软件的交互提供了洞见，并推荐了设计更易获取的预测软件的方法。