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The effects of example-based explanations in a machine learning interface
Open accessAbstract
The black-box nature of machine learning algorithms can make their predictions difficult to understand and explain to end-users. In this paper, we propose and evaluate two kinds of example-based explanations in the visual domain, normative explanations and comparative explanations (Figure 1), which automatically surface examples from the training set of a deep neural net sketch-recognition algorithm. To investigate their effects, we deployed these explanations to 1150 users on QuickDraw, an online platform where users draw images and see whether a recognizer has correctly guessed the intended drawing. When the algorithm failed to recognize the drawing, those who received normative explanations felt they had a better understanding of the system, and perceived the system to have higher capability. However, comparative explanations did not always improve perceptions of the algorithm, possibly because they sometimes exposed limitations of the algorithm and may have led to surprise. These findings suggest that examples can serve as a vehicle for explaining algorithmic behavior, but point to relative advantages and disadvantages of using different kinds of examples, depending on the goal.
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“Wang and Yin [82] found that example-based explanations underperform feature-based explanations for improving appropriate reliance, though other studies suggest example-based explanations are better at helping people detect model errors [11,13,49], usually by recognizing the dissimilarity between the instance and selected examples.”
From Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) · cites this paper as [13]
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“A study conducted by Cai et al. [3] found that comparative explanation, by comparisons with examples in alternative classes, can lead to better discovery of the limitations of the AI, compared to normative explanation that describes examples in the intended class.”
From Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making (2020) · cites this paper as [3]
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Self-citation
Metadata
- Authors
- Cai, C. J., Jongejan, J., & Holbrook, J.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the 24th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2019-03-17
- Pages
- 258-262
- OpenAlex
- W2916797267
- Cited by
- 220
- References
- 32
- Citations by year
- 2026: 13 · 2025: 26 · 2024: 42 · 2023: 38 · 2022: 35 · 2021: 34 · 2020: 28 · 2019: 4
- FWCI
- 9.66
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- example-based explanations, explainable machine learning, normative explanation, sketch recognition, deep neural network, black-box algorithms, QuickDraw
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