The DOI matches its Crossref record; 65 of the 68 references it cites were found in a registry or an index.
Making deep neural networks right for the right scientific reasons by interacting with their explanations
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“The subjective trust in the AI system was assessed using the Trust in Automation questionnaire (TiA) [63], which is a commonly employed and validated tool for measuring trust [78, 116, 120].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [116]
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Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Learning Deep Features for Discriminative Localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428
A tutorial on spectral clustering
Ulrike Von Luxburg, A. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9033-z · Cited by 10,787
Research works printed but not linked: 6
- Herbert, F. P., Kersting, K. & Jäkel, F. Why should I trust in AI? Master’s thesis (Technical Univ. Darmstadt, 2019).
- Cakmak, M. et al. Mixed-initiative active learning. In ICML 2011 Workshop on Combining Learning Strategies to Reduce Label Cost (ACM, 2011).
- Roy, N. et al. Toward optimal active learning through Monte Carlo estimation of error reduction. In International Conference for Machine Learning 441–448 (ICML, 2001).
- Castro, R. M. et al. Upper and lower error bounds for active learning. In Proc. Conference on Communication, Control and Computing 1 (Univ. Illinois, 2007).
- Tong, S. & Koller, D. Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45–66 (2001).
- Meier, U. et al. Phenological growth stages of sugar beet (Beta vulgaris l. ssp.) codification and description according to the general BBCH scale (with figures). Nachr. Dtsch. Pflanzenschutzd. 45, 37–41 (1993).
Cited by
Sorted by citations
Items 201–205 of 205
RAIN: An embarrassingly simple approach to debiasing attribution evaluation
Duan, J., Song, J., Zhang, H., Xue, M., Wang, H., & Song, M. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104773
Heim, R., Garg, P. K., Kumar, P., Mattioli, D., & Kappas, M. W. (2026) · Data-intensive research · doi:10.1007/978-3-032-20311-3_12
XAI or Attention: Improving Performance of Object Detectors with XBL
Zhukov, A., Benois‐Pineau, J., Youssef, A., Zemmari, A., Mosbah, M., & Taillandier, V. (2026) · Proceedings - International Conference on Image Processing · doi:10.1109/icip61757.2026.11630047
A Multi-Scale Fractal Feature Extraction Method for CNN-Based Plant Disease Classification
Savchenko, E. T., & Maslovskaya, A. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8090273
Abetu, M. T., Urgessa, T., & Tune, K. K. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200744
Metadata
- Authors
- Schramowski, P., Stammer, W., Teso, S., Brugger, A., Herbert, F., Shao, X., Luigs, H.-G., Mahlein, A.-K., & Kersting, K.
- Affiliations
- Technische Universität Darmstadt (DE)
- University of Trento (IT)
- University of Bonn (DE)
- Corresponding author
- Schramowski, Patrick
- ORCID
- Venue
- Nature Machine Intelligence
- Published
- 2020-08-12
- Volume
- 2
- Issue
- 8
- Pages
- 476-486
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3035989815
- Cited by
- 207
- References
- 68
- Citations by year
- 2026: 11 · 2025: 33 · 2024: 45 · 2023: 46 · 2022: 31 · 2021: 32 · 2020: 9
- FWCI
- 11.58
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Iris (University of Trento) (submittedVersion, other-oa)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Code Ocean
- Funders
- European Commission (694980)
- Deutsche Forschungsgemeinschaft (EXC-2070, 390732324)
- Bundesministerium für Ernährung und Landwirtschaft (2818204715)
- Bundesamt für Landwirtschaft
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- human-in-the-loop learning, model explanation, Clever Hans effect, confounding factors, plant phenotyping, deep neural network
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