Effectiveness of Example-Based Explanations to Improve Human Decision Quality in Machine Learning Forecasting Systems
Type
conference paper
Date Issued
2022-12-09
Author(s)
Abstract
Algorithmic forecasts outperform human forecasts by 10% on average. State-of-the-art machine learning (ML) algorithms have further expanded this discrepancy. Because a variety of other activities rely on them, sales forecasting is critical to a company's profitability. However, individuals are hesitant to use ML forecasts. To overcome this algorithm aversion, explainable artificial intelligence (XAI) can be a solution by making ML systems more comprehensible by providing explanations. However, current XAI techniques are incomprehensible for laymen, as they impose too much cognitive load. We contribute to this research gap by investigating the effectiveness in terms of forecast accuracy of two example-based explanation approaches. We conduct an online experiment based on a two-by-two between-subjects design with factual and counterfactual examples as experimental factors. A control group has access to ML predictions, but not to explanations. We report results of this study: While factual explanations significantly improved participants' decision quality, counterfactual explanations did not.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SoM - Business Innovation
Event Title
International Conference on Information Systems (ICIS)
Event Location
Copenhagen, Denmark
Event Date
09-12 Dec 2022
Subject(s)
Division(s)
Eprints ID
267615
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Name
icis22b-sub1273-cam-i9.pdf
Size
1.29 MB
Format
Adobe PDF
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