Algorithmic Transparency in Forecasting Support Systems
This addresses the problem of optimizing manual forecast adjustments in organizational settings, offering incremental insights into FSS design trade-offs.
The paper investigates whether algorithmic transparency in Forecasting Support Systems (FSS) improves forecast adjustments, finding that it reduces harmful adjustments but can overwhelm users without training, leading to varied and detrimental outcomes when users adjust transparent components themselves.
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.