Explainable AI improves task performance in human–AI collaboration

Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human-AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed [Formula: see text] assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed [Formula: see text] assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, [Formula: see text]) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, [Formula: see text]) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.

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Publication Details

Journal
Scientific Reports
Published
2024-12-28
DOI
https://doi.org/10.1038/s41598-024-82501-9
Citations
53
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
18.41

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article

Explainable AI improves task performance in human–AI collaboration

Simon Schallmoser, Bernhard Kratzwald, Julian Senoner, Stefan Feuerriegel et al.
53 citations
Scientific Reports
Explainable Artificial Intelligence (XAI)
18.41
article

Explainable AI improves task performance in human–AI collaboration

Simon Schallmoser, Bernhard Kratzwald, Julian Senoner, Stefan Feuerriegel, Torbjørn H. Netland
article en
53 citations

Abstract

Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human-AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed [Formula: see text] assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed [Formula: see text] assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, [Formula: see text]) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, [Formula: see text]) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.

Scientific ReportsVol. 14(1)
LMU Klinikum (DE), ETH Zurich (CH), Munich Center for Machine Learning, Ludwig-Maximilians-Universität München (DE)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Openalex Percentile: Top 1%
Explainable Artificial Intelligence (XAI)
18.41
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