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Explanation Interfaces for Sales Forecasting

Type
conference paper
Date Issued
2022-06-18
Author(s)
Fahse, Tobias  
Blohm, Ivo  
Hruby, Richard
van Giffen, Benjamin  
Abstract
Algorithmic forecasts outperform human forecasts in many tasks. State-of-the-art machine learning (ML) algorithms have even widened that gap. Since sales forecasting plays a key role in business profitability, ML based sales forecasting can have significant advantages. However, individuals are resistant to use algorithmic forecasts. To overcome this algorithm aversion, explainable AI (XAI), where an explanation interface (XI) provides model predictions and explanations to the user, can help. However, current XAI techniques are incomprehensible for laymen. Despite the economic relevance of sales forecasting, there is no significant research effort towards aiding non-expert users make better decisions using ML forecasting systems by designing appropriate XI. We contribute to this research gap by designing a model-agnostic XI for laymen. We propose a design theory for XIs, instantiate our theory and report initial formative evaluation results. A real-world evaluation context is used: A medium-sized Swiss bakery chain provides past sales data and human forecasts.
Language
English
Keywords
Forecasting
Explainable AI
XAI
Design Science
HSG Classification
contribution to scientific community
HSG Profile Area
SoM - Business Innovation
Publisher
Association for Information Systems
Event Title
European Conference on Information Systems 2022
Event Location
Timisoara; Romania
Event Date
18-24.06.2022
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/108601
Subject(s)

information managemen...

business studies

Division(s)

IWI - Institute of In...

Contact Email Address
tobias.fahse@unisg.ch
Eprints ID
266643
File(s)
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Thumbnail Image

open.access

Name

Explanation Interfaces for Sales Forecasting_final.pdf

Size

1.03 MB

Format

Adobe PDF

Checksum (MD5)

17131994e613f1c707874794315bd83c

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