AcME - accelerated model-agnostic explanations: fast whitening of the machine-learning black box

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works

Greedy function approximation: A gradient boosting machine

Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works

The magical number seven, plus or minus two: Some limits on our capacity for processing information.

Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812 · see citing works

Advances in Neural Information Processing Systems 29

Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works

Machine learning: Trends, perspectives, and prospects

Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Conditional variable importance for random forests

Strobl, C., Boulesteix, A., Kneib, T., Augustin, T., & Zeileis, A. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-307 · Cited by 3,299 · see citing works

Towards A Rigorous Science of Interpretable Machine Learning

Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works

Definitions, methods, and applications in interpretable machine learning

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works

Anchors: High-Precision Model-Agnostic Explanations

Ribeiro, M. T., Singh, S., & Guestrin, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11491 · Cited by 2,162 · see citing works

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics 2016

Murray, I., & Graham, M. (2016) · Cited by 2,079 · see citing works

Crop yield prediction using machine learning: A systematic literature review

van Klompenburg, T., Kassahun, A., & Catal, C. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105709 · Cited by 1,881 · see citing works

The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI

Shin, D. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102551 · Cited by 1,548 · see citing works

Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis

Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. (2019) · Accident Analysis & Prevention · doi:10.1016/j.aap.2019.105405 · Cited by 1,065 · see citing works

From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

Ahmed, I., Jeon, G., & Piccialli, F. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3146552 · Cited by 853 · see citing works

Explaining anomalies detected by autoencoders using Shapley Additive Explanations

Antwarg, L., Miller, R. M., Shapira, B., & Rokach, L. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115736 · Cited by 496 · see citing works

A Theoretical Analysis of NDCG Type Ranking Measures

Wang, Y., Wang, L., Li, Y., He, D., Liu, T.-Y., & Chen, W. (2013) · arXiv · doi:10.48550/arxiv.1304.6480 · Cited by 351 · see citing works

Interpretable Machine Learning

Boehmke, B., & Greenwell, B. (2019) · Chapman & Hall/CRC eBooks · doi:10.1201/9780367816377-16 · Cited by 271 · see citing works

Interpreting tree ensembles with inTrees

Deng, H. (2018) · International Journal of Data Science and Analytics · doi:10.1007/s41060-018-0144-8 · Cited by 254 · see citing works

Machine learning applications in production lines: A systematic literature review

Kang, Z., Catal, C., & Teki̇nerdoğan, B. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106773 · Cited by 244 · see citing works

Problems with Shapley-value-based explanations as feature importance measures

Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. (2020) · arXiv · doi:10.48550/arxiv.2002.11097 · Cited by 228 · cited 1× · see citing works

Shapley-Lorenz eXplainable Artificial Intelligence

Giudici, P., & Raffinetti, E. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114104 · Cited by 202 · see citing works

Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems

Andras, P., Esterle, L., Guckert, M., Han, T. A., Lewis, P. R., Milanovic, K., Payne, T., Perret, C., Pitt, J., Powers, S. T., et al. (2018) · IEEE Technology and Society Magazine · doi:10.1109/mts.2018.2876107 · Cited by 153 · see citing works

Explainable Artificial Intelligence for Tabular Data: A Survey

Sahakyan, M., Aung, Z., & Rahwan, T. (2021) · IEEE Access · doi:10.1109/access.2021.3116481 · Cited by 135 · see citing works

Explaining instance classifications with interactions of subsets of feature values

Štrumbelj, E., Kononenko, I., & Robnik-Sikonja, M. (2009) · Data & Knowledge Engineering · doi:10.1016/j.datak.2009.01.004 · Cited by 129 · see citing works

Interpretable Anomaly Detection with DIFFI: Depth-based feature importance of Isolation Forest

Carletti, M., Terzi, M., & Susto, G. A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105730 · Cited by 118 · see citing works

Social media rumor refutation effectiveness: Evaluation, modelling and enhancement

Li, Z., Zhang, Q., Du, X., Ma, Y., & Wang, S. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102420 · Cited by 113 · see citing works

Explainable AI meets Healthcare: A Study on Heart Disease DatasetPreprint

Dave, D., Naik, H., Singhal, S., & Patel, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03195 · Cited by 104 · see citing works

When machine learning meets medical world: Current status and future challenges

Smiti, A. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100280 · Cited by 79 · see citing works

Interpreting Remaining Useful Life estimations combining Explainable Artificial Intelligence and domain knowledge in industrial machinery

Serradilla, O., Zugasti, E., Cernuda, C., Aranburu, A., de Okariz, J. R., & Zurutuza, U. (2020) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz48607.2020.9177537 · Cited by 53 · see citing works

A framework to analyze the emotional reactions to mass violent events on Twitter and influential factors

Harb, J. G. D., Ebeling, R., & Becker, K. E. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102372 · Cited by 44 · see citing works

A Survey of Approximate Quantile Computation on Large-Scale Data

Chen, Z., & Zhang, A. (2020) · IEEE Access · doi:10.1109/access.2020.2974919 · Cited by 19 · see citing works

EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees

Yoo, J., & Sael, L. (2019) · Proceedings · doi:10.1109/icdm.2019.00187 · Cited by 8 · see citing works

Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges

Patil, S. S., Jha, G. K., Mohsin, N., Patel, P., Ralte, L., & Shukla, A. K. (2026) · Cureus · doi:10.7759/cureus.109905

Demystifying Black Box Models with Neural Networks for Accuracy and Interpretability of Supervised Learning

Prabhakaran, J. (2018) · doi:10.2991/pecteam-18.2018.22 · Cited by 1 · see citing works

Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box ModelPreprint

Wang, T., & Lin, Q. (2019) · arXiv.org · doi:10.48550/arxiv.1905.04241 · Cited by 19 · see citing works

A Theory of Diagnostic Interpretation in Supervised ClassificationPreprint

Mukhopadhyay, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.10080 · Cited by 1 · see citing works

Innovative approaches to addressing the tradeoff between interpretability and accuracy in ship fuel consumption prediction

Wang, H., Yan, R., Wang, S., & Zhen, L. (2023) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2023.104361 · Cited by 84 · see citing works

Not Just a Black Box: Learning Important Features Through Propagating Activation DifferencesPreprint

Shrikumar, A., Greenside, P., Shcherbina, A., & Kundaje, A. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.01713 · Cited by 704 · see citing works