提升预测系统透明度可减少错误调整，但过度透明反而让用户负担过重。
Algorithmic Transparency in Forecasting Support Systems
- 通过时间序列分解设计三种透明度不同的预测系统界面
- 透明度越高，有害调整的频率和波动越低
- 让用户自行调整透明组件会引发更差的预测结果
多数组织会人工调整统计预测（如销售预测）。预测支持系统（FSS）实现了自动化生成预测与人工修正的流程。由于FSS界面连接用户与统计算法，它成为促进有益调整、抑制有害调整的关键杠杆。本文综述并整合了判断性预测、预测修正及FSS设计领域的文献，提出算法透明度是实现更好整合性预测的关键因素，并通过三种基于时间序列分解、透明度不同的FSS设计进行验证。结果表明，提高透明度能降低有害调整的方差与数量；然而，允许用户直接修改透明组件后，调整行为差异极大，整体效果最差。用户反馈显示，缺乏培训时过度透明反而造成认知负荷，非透明系统带来的自评满意度最高。
原文摘要 · Abstract (English)
Most organizations adjust their statistical forecasts (e.g. on sales) manually. Forecasting Support Systems (FSS) enable the related process of automated forecast generation and manual adjustments. As the FSS user interface connects user and statistical algorithm, it is an obvious lever for facilitating beneficial adjustments whilst discouraging harmful adjustments. This paper reviews and organizes the literature on judgemental forecasting, forecast adjustments, and FSS design. I argue that algorithmic transparency may be a key factor towards better, integrative forecasting and test this assertion with three FSS designs that vary in their degrees of transparency based on time series decomposition. I find transparency to reduce the variance and amount of harmful forecast adjustments. Letting users adjust the algorithm's transparent components themselves, however, leads to widely varied and overall most detrimental adjustments. Responses indicate a risk of overwhelming users with algorithmic transparency without adequate training. Accordingly, self-reported satisfaction is highest with a non-transparent FSS.
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