Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning
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Cites
Lipton, P. (1990) · Royal Institute of Philosophy Supplement · doi:10.1017/s1358246100005130 · Cited by 343 · see citing works
Savický, P., Klaschka, J., & Antoch, J. (2000) · COMPSTAT · doi:10.1007/978-3-642-57678-2_58 · Cited by 6 · see citing works
Model Visualization Techniques and Traditional Interpretable Algorithms
Kamath, U., & Liu, J. (2021) · Springer eBooks · doi:10.1007/978-3-030-83356-5_3 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
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A Review of Trustworthy and Explainable Artificial Intelligence (XAI)
Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3294569 · Cited by 352 · see citing works
“interpretable learning algorithms [42].”
Robustness Analysis of Deep Learning-Based Lung Cancer Classification Using Explainable Methods
Malafaia, M., Silva, F., Neves, I., Pereira, T., & Oliveira, H. P. (2022) · IEEE Access · doi:10.1109/access.2022.3214824 · Cited by 25 · see citing works
The Trust Recovery Journey. The Effect of Timing of Errors on the Willingness to Follow AI Advice.
Kahr, P. K., Rooks, G., Snijders, C., & Willemsen, M. C. (2024) · Proceedings of the 29th International Conference on Intelligent User Interfaces · doi:10.1145/3640543.3645167 · Cited by 21 · see citing works
Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data
Chatterjee, S., Mishra, J., Sundram, F., & Roop, P. (2023) · Sensors · doi:10.3390/s24010164 · Cited by 15 · see citing works
- Authors
- Kamath, U., & Liu, J.
- ORCID
- Liu, John
- Venue
- Springer eBooks
- Publisher
- Springer International Publishing
- Published
- 2021
- OpenAlex
- W4205820386
- Cited by
- 126
- References
- 3
- Citations by year
- 2026: 9 · 2025: 30 · 2024: 41 · 2023: 36 · 2022: 10
- FWCI
- 9.98
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- explainable AI, model interpretability, model transparency, white-box models, black-box models, neural network interpretability, feature sensitivity analysis, gradient analysis, perturbation analysis, difference analysis, dependency analysis, natural language processing, computer vision, time series, human-machine collaboration, causal machine learning, explainable security, responsible AI, AI compliance, trustworthiness
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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