Algorithmic Transparency in Forecasting Support Systems
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Applying Transparency in Artificial Intelligence based Personalization Systems
Artificial Intelligence based systems increasingly use personalization to provide users with relevant content, products, and solutions. Personalization is intended to support users and address their respective needs and …
EthicsAccountability Capture: How Record-Keeping to Support AI Transparency and Accountability (Re)shapes Algorithmic Oversight
Accountability regimes typically encourage record-keeping to enable the transparency that supports oversight, investigation, contestation, and redress. However, implementing such record-keeping can introduce consideratio…
Towards Algorithmic Transparency: A Diversity Perspective
As the role of algorithmic systems and processes increases in society, so does the risk of bias, which can result in discrimination against individuals and social groups. Research on algorithmic bias has exploded in rece…
DiversityFairnessManagementLogics and practices of transparency and opacity in real-world applications of public sector machine learning
Machine learning systems are increasingly used to support public sector decision-making across a variety of sectors. Given concerns around accountability in these domains, and amidst accusations of intentional or uninten…
BIG-bench Machine LearningDecision MakingFairnessAn Algorithmic Equity Toolkit for Technology Audits by Community Advocates and Activists
A wave of recent scholarship documenting the discriminatory harms of algorithmic systems has spurred widespread interest in algorithmic accountability and regulation. Yet effective accountability and regulation is stymie…