  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.
