The mythos of model interpretability

Cited by (1,043)

Items 201–300 of 1,043

1. Interpretable Machine Learning
   Brad Boehmke, Brandon Greenwell · 2019 · Chapman & Hall/CRC eBooks
2. Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to?
   Alberto Fernández, Francisco Herrera, Óscar Cordón, María José del Jesús, Francesco Marcelloni · 2019 · IEEE Computational Intelligence Magazine
3. In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions
   Andrea Ferrario, Michele Loi, Eleonora Viganò · 2019 · Philosophy & Technology
4. Hydrological concept formation inside long short-term memory (LSTM) networks
   Thomas Lees, Steven Reece, Frederik Kratzert, Daniel Klotz, Martin Gauch, Jens de Bruijn, Reetik Kumar Sahu, Peter Greve, Louise J. Slater, Simon Dadson · 2022 · Hydrology and earth system sciences
5. Limits of trust in medical AI
   Joshua James Hatherley · 2020 · Journal of Medical Ethics
6. Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation
   Feier Wang, Yixu Wang, Kai Zhang, Ming Hu, Qin Weng, Huichun Judy Zhang · 2021 · Environmental Research
7. Troubling Trends in Machine Learning Scholarship: Some ML papers suffer from flaws that could mislead the public and stymie future research.
   Lipton, Zachary C., Steinhardt, Jacob · 2019 · Queue
8. Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges
   Zablocki, Éloi, Ben-Younes, Hédi, Pérez, Patrick, Cord, Matthieu · 2022 · International Journal of Computer Vision
9. Explainability in human–agent systems
   Avi Rosenfeld, Ariella Richardson · 2019 · Autonomous Agents and Multi-Agent Systems
10. Gamut
    Fred Hohman, Andrew Head, Rich Caruana, Robert A DeLine, Steven M. Drucker · 2019 · CHI Conference on Human Factors in Computing Systems
11. Explainable artificial intelligence in disaster risk management: Achievements and prospective futures
    Saman Ghaffarian, Firouzeh Rosa Taghikhah, Holger Robert Maier · 2023 · International Journal of Disaster Risk Reduction
12. Defining the undefinable: the black box problem in healthcare artificial intelligence
    Wadden, Jordan Joseph · 2022 · Journal of Medical Ethics
13. Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects
    Passi, Samir, Jackson, Steven J. · 2018 · Proceedings of the ACM on Human-Computer Interaction
14. Learning Molecular Representations for Medicinal Chemistry
    Kangway V. Chuang, Laura M. Gunsalus, Michael J. Keiser · 2020 · Journal of Medicinal Chemistry
15. Explainability as a non-functional requirement: challenges and recommendations
    Larissa Chazette, Kurt A. Schneider · 2020 · Requirements Engineering
16. Against Interpretability: a Critical Examination of the Interpretability Problem in Machine Learning
    Maya Krishnan · 2019 · Philosophy & Technology
17. Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations
    Fred Hohman, Haekyu Park, Caleb Robinson, Duen Horng Chau · 2019 · IEEE Transactions on Visualization and Computer Graphics
18. Human factors in model interpretability: Industry practices, challenges, and needs
    Hong, Sungsoo Ray, Hullman, Jessica, Bertini, Enrico · 2020 · Proceedings of the ACM on Human-Computer Interaction
19. An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference
    Mohannad Zeyad Naser · 2021 · Automation in Construction
20. Antibody structure prediction using interpretable deep learning
    Ruffolo, Jeffrey A., Sulam, Jeremias, Gray, Jeffrey J. · 2022 · Patterns
21. Investigating Explainability of Generative AI for Code through Scenario-based Design
    Sun, Jiao, Liao, Q. Vera, Muller, Michael, Agarwal, Mayank, Houde, Stephanie, Talamadupula, Kartik, Weisz, Justin D. · 2022 · 27th International Conference on Intelligent User Interfaces
22. MODE: automated neural network model debugging via state differential analysis and input selection
    Shiqing Ma, Yingqi Liu, Wen‐Chuan Lee, Xiangyu Zhang, Ananth Y. Grama · 2018 · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)
23. Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
    Andreas Holzinger, Matthias Dehmer, Frank Emmert‐Streib, Rita Cucchiara, Isabelle Augenstein, Javier Del Ser, Wojciech Samek, Igor Jurišica, Natalia Díaz-Rodríguez · 2021 · Information Fusion
24. Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
    Wu, Mike, Hughes, Michael, Parbhoo, Sonali, Zazzi, Maurizio, Roth, Volker, Doshi-Velez, Finale · 2018 · Proceedings of the AAAI Conference on Artificial Intelligence
25. A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges
    Mienye, Ibomoiye Domor, Obaido, George, Jere, Nobert, Mienye, Ebikella, Aruleba, Kehinde, Emmanuel, Ikiomoye Douglas, Ogbuokiri, Blessing · 2024 · Informatics in Medicine Unlocked
26. Adversarial Examples: Attacks and Defenses for Deep Learning
    Xiaoyong Yuan, Pan Li He, Qile Zhu, Xiaolin Li · 2017 · arXiv (Cornell University)
27. General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
    Molnar, Christoph, König, Gunnar, Herbinger, Julia, Freiesleben, Timo, Dandl, Susanne, Scholbeck, Christian A., Casalicchio, Giuseppe, Grosse-Wentrup, Moritz, Bischl, Bernd · 2022 · Lecture Notes in Computer Science
28. Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies
    Kenny, Eoin M., Ford, Courtney, Quinn, Molly, Keane, Mark T. · 2021 · Artificial Intelligence
29. Post-hoc Interpretability for Neural NLP: A Survey
    Andreas Madsen, Siva Reddy, Sarath Chandar · 2022 · ACM Computing Surveys
30. Epigenetic ageing clocks: statistical methods and emerging computational challenges
    Andrew Erich Teschendorff, Steve Horvath · 2025 · Nature Reviews Genetics
31. Automated rationale generation: a technique for explainable AI and its effects on human perceptions
    Ehsan, Upol, Tambwekar, Pradyumna, Chan, Larry, Harrison, Brent, Riedl, Mark O. · 2019 · Proceedings of the 24th International Conference on Intelligent User Interfaces
32. Disentangled Graph Convolutional Networks
    Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu · 2019 · International Conference on Machine Learning
33. A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
    Subrato Bharati, Md. Rubaiyat Hossain Mondal, Prajoy Podder · 2023 · IEEE Transactions on Artificial Intelligence
34. Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery
    Tison, Geoffrey H., Zhang, Jeffrey, Delling, Francesca N., Deo, Rahul C. · 2019 · Circulation: Cardiovascular Quality and Outcomes
35. Designing interpretable ML system to enhance trust in healthcare: A systematic review to proposed responsible clinician-AI-collaboration framework
    Nasarian, Elham, Alizadehsani, Roohallah, Acharya, U.Rajendra, Tsui, Kwok-Leung · 2024 · Information Fusion
36. Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives
    Sebastian Gehrmann, Franck Dernoncourt, Yeran Li, Eric T. Carlson, Joy Tzung-Yu Wu, Jonathan A. Welt, John Foote, Edward T. Moseley, David W. Grant, Patrick D. Tyler et al. · 2018 · PLoS ONE
37. A Review on Explainability in Multimodal Deep Neural Nets
    Joshi, Gargi, Walambe, Rahee, Kotecha, Ketan · 2021 · IEEE Access
38. Interpretable machine learning
    Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar · 2022 · Communications of the ACM
39. Human and AI Ensembles: When Can They Work?
    Vivek Choudhary, Arianna Marchetti, Yash Raj Shrestha, Phanish Puranam · 2021 · Journal of Management
40. The Challenges of Machine Learning: A Critical Review
    Enrico Barbierato, Alice Gatti · 2024 · Electronics
41. Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
    Mascharka, David, Tran, Philip, Soklaski, Ryan, Majumdar, Arjun · 2018 · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
42. Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
    Andy Brown, Aaron R. Tuor, Brian Hutchinson, Nicole Nichols · 2018 · Proceedings of the First Workshop on Machine Learning for Computing Systems
43. iForest: Interpreting Random Forests via Visual Analytics
    Xun Zhao, Yanhong Wu, Dik Lun Lee, Weiwei Cui · 2018 · IEEE Transactions on Visualization and Computer Graphics
44. Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
    Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju · 2019 · arXiv (Cornell University)
45. Challenges and frontiers in abusive content detection
    Bertie Vidgen, Alex Harris, Dong Nguyen, Rebekah Tromble, Scott A. Hale, Helen Zerlina Margetts · 2019 · Workshop on Abusive Language Online (ALW)
46. Integrating Transparency, Trust, and Acceptance: The Intelligent Systems Technology Acceptance Model (ISTAM)
    Eric S. Vorm, David J. Y. Combs · 2022 · International Journal of Human-Computer Interaction
47. An attention based deep learning model of clinical events in the intensive care unit
    Deepak A. Kaji, John R. Zech, Jun Kim, Samuel Kang-Wook Cho, Neha Subhash Dangayach, Anthony Beardsworth Costa, Eric Karl Oermann · 2019 · PLoS ONE
48. Explainable Recommendation: A Survey and New Perspectives
    Yongfeng Zhang, Xu Chen · 2020
49. Artificial Communication
    Elena Esposito · 2022 · The MIT Press eBooks
50. Toward Edge-Based Deep Learning in Industrial Internet of Things
    Fan Liang, Wei Yu, Xing Liu, DAVID W. T. GRIFFITH, Nada Golmie · 2020 · IEEE Internet of Things Journal
51. Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions
    Andreas Theissler, Francesco Spinnato, Udo Schlegel, Riccardo Guidotti · 2022 · IEEE Access
52. Explainable AI for Bioinformatics: Methods, Tools and Applications
    Md. Rezaul Karim, Tanhim Islam, Md Shajalal, Oya Beyan, Christoph Lange, Michael Cochez, Dietrich Rebholz‐Schuhmann, Stefan Decker · 2023 · Briefings in Bioinformatics
53. Transparency: Motivations and Challenges
    Weller, Adrian · 2019 · Lecture Notes in Computer Science
54. Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases
    Joseph C. Ahn, Alistair Connell, Douglas Alano Simonetto, Cían Hughes, Vijay H. Shah · 2020 · Hepatology
55. A review of some techniques for inclusion of domain-knowledge into deep neural networks
    Dash, Tirtharaj, Chitlangia, Sharad, Ahuja, Aditya, Srinivasan, Ashwin · 2022 · Scientific Reports
56. Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence
    Anna Katharina Bonkhoff, Christian Grefkes · 2021 · Brain
57. Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task
    Leichtmann, Benedikt, Humer, Christina, Hinterreiter, Andreas, Streit, Marc, Mara, Martina · 2023 · Computers in Human Behavior
58. The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
    Laugel, Thibault, Lesot, Marie-Jeanne, Marsala, Christophe, Renard, Xavier, Detyniecki, Marcin · 2019 · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
59. The role of shared mental models in human-AI teams: a theoretical review
    Robert W. Andrews, J. Mason Lilly, Divya Srivastava, Karen M. Feigh · 2022 · Theoretical Issues in Ergonomics Science
60. A Survey of Explainable Artificial Intelligence for Smart Cities
    Abdul Rehman Javed, Waqas Ahmed, Sharnil Pandya, Praveen Kumar Reddy Maddikunta, Mamoun Alazab, Thippa Reddy Gadekallu · 2023 · Electronics
61. Transparency in Complex Computational Systems
    Kathleen Creel · 2020 · Philosophy of Science
62. Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making
    Rastogi, Charvi, Zhang, Yunfeng, Wei, Dennis, Varshney, Kush R., Dhurandhar, Amit, Tomsett, Richard · 2022 · Proceedings of the ACM on Human-Computer Interaction
63. Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches
    Cheng Fan, Da Yan, Fu Xiao, Ao Li, Jingjing An, Xuyuan Kang · 2020 · Building Simulation
64. Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
    Tirtha Chanda, Katja Hauser, Sarah Hobelsberger, Tabea-Clara Bucher, Carina Nogueira Garcia, Christoph Wies, Harald Kittler, Philipp Tschandl, Cristián Navarrete‐Dechent, Sebastián Podlipnik et al. · 2024 · Nature Communications
65. Interpretability in healthcare: A comparative study of local machine learning interpretability techniques
    Radwa Elshawi, Youssef Sherif, Mouaz Husayn Al-Mallah, Sherif Sakr · 2020 · Computational Intelligence
66. Advancing Computational Toxicology by Interpretable Machine Learning
    Xuelian Jia, Tong Wang, Hao Zhu · 2023 · Environmental Science & Technology
67. Explainable artificial intelligence enhances the ecological interpretability of black‐box species distribution models
    Masahiro Ryo, Boyan Angelov, Stefano Mammola, Jamie M. Kass, Blas M. Benito, Florian Hartig · 2020 · Ecography
68. A systematic review of personal thermal comfort models
    Larissa Arakawa Martins, Veronica I. Soebarto, Terence Williamson · 2021 · Building and Environment
69. Explainable AI under Contract and Tort Law: Legal Incentives and Technical Challenges
    Philipp Hacker, Ralf Krestel, Stefan Grundmann, Felix Naumann · 2020 · Artificial Intelligence and Law
70. Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI
    Tomsett, Richard, Preece, Alun, Braines, Dave, Cerutti, Federico, Chakraborty, Supriyo, Srivastava, Mani, Pearson, Gavin, Kaplan, Lance · 2020 · Patterns
71. Model-Agnostic Counterfactual Explanations for Consequential Decisions
    Amir-Hossein Karimi, Gilles Barthe, Borja Balle, Isabel Valera · 2019 · arXiv (Cornell University)
72. A causal framework for explaining the predictions of black-box sequence-to-sequence models
    David Alvarez-Melis, Tommi Jaakkola · 2017 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
73. Clinical concept extraction: A methodology review
    Sunyang Fu, David J. Chen, Huan He, Sijia Liu, Sungrim Moon, Kevin J. Peterson, Feichen Shen, Liwei Wang, Yanshan Wang, Andrew Wen et al. · 2020 · Journal of Biomedical Informatics
74. Roadmap on Machine learning in electronic structure
    Heather J. Kulik, Thomas Hammerschmidt, Jonathan Schmidt, Silvana Botti, Miguel A. L. Marques, Mario Boley, Matthias Scheffler, Milica Todorović, Patrick Rinke, Corey Oses et al. · 2022 · Electronic Structure
75. Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information
    Kim, Buomsoo, Park, Jinsoo, Suh, Jihae · 2020 · Decision Support Systems
76. A literature review on applications of explainable artificial intelligence (XAI)
    Kalasampath, Khushi, Spoorthi, K. N., Sajeev, Sreeparvathy, Kuppa, Sahil Sarma, Ajay, Kavya, Maruthamuthu, Angulakshmi · 2025 · IEEE Access
77. From explainable to interactive AI: A literature review on current trends in human-AI interaction
    Raees, Muhammad, Meijerink, Inge, Lykourentzou, Ioanna, Khan, Vassilis-Javed, Papangelis, Konstantinos · 2024 · International Journal of Human-Computer Studies
78. Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients
    Ma, Jianzhu, Fong, Samson H., Luo, Yunan, Bakkenist, Christopher J., Shen, John Paul, Mourragui, Soufiane, Wessels, Lodewyk F. A., Hafner, Marc, Sharan, Roded, Peng, Jian et al. · 2021 · Nature Cancer
79. Modeling Missing Data in Clinical Time Series with RNNs
    Zachary C. Lipton, David C. Kale, Randall C. Wetzel · 2016 · arXiv (Cornell University)
80. Sociotechnical Envelopment of Artificial Intelligence: An Approach to Organizational Deployment of Inscrutable Artificial Intelligence Systems
    Aleksandre Asatiani, Pekka Malo, Per Rådberg Nagbøl, E. Penttinen, Tapani Rinta-Kahila, A. Salovaara · 2021 · Journal of the AIS
81. Interpretable machine learning for predicting and evaluating hydrogen production via supercritical water gasification of biomass
    Sheng Hui Zhao, Jian Li, Chao Chen, Beibei Yan, Junyu Tao, Guanyi Chen · 2021 · Journal of Cleaner Production
82. Challenges and opportunities for artificial intelligence in auditing: Evidence from the field
    Kokina, Julia, Blanchette, Shay, Davenport, Thomas H., Pachamanova, Dessislava · 2025 · International Journal of Accounting Information Systems
83. Comparing Automatic and Human Evaluation of Local Explanations for Text Classification
    Dong Nguyen · 2018 · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)
84. An explainable AI decision-support-system to automate loan underwriting
    Swati Sachan, Jianbo Yang, Dong‐Ling Xu, David Eraso Benavides, Yang Li · 2019 · Expert Systems with Applications
85. Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions
    Luca Longo, Randy G. Goebel, Freddy Lécué, Peter Kieseberg, Andreas Holzinger · 2020 · Lecture notes in computer science
86. Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset
    Chuizheng Meng, Loc Trinh, Nan Xu, James Enouen, Yan Liu · 2022 · Scientific Reports
87. Explainable artificial intelligence and interpretable machine learning for agricultural data analysis
    Masahiro Ryo · 2022 · Artificial Intelligence in Agriculture
88. Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques
    Zaineb Mohammed Alhakeem, Yasir Mohammed Jebur, Sadiq Naama Henedy, Hamza Imran, Luís Filipe Almeida Bernardo, Hussein M. Hussein · 2022 · Materials
89. Predictive maintenance using tree-based classification techniques: A case of railway switches
    Zaharah Allah Bukhsh, Aaqib Saeed, Irina Stipanović, Andries G. Dorée · 2019 · Transportation Research Part C Emerging Technologies
90. From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies
    Parvathaneni Naga Srinivasu, N. Sandhya, Rutvij H. Jhaveri, Roshani Raut · 2022 · Mobile Information Systems
91. How does the model make predictions? A systematic literature review on the explainability power of machine learning in healthcare
    Johannes Allgaier, Lena Mulansky, Rachel Lea Draelos, Rüdiger C. Pryss · 2023 · Artificial Intelligence in Medicine
92. Pre-trained Language Models in Biomedical Domain: A Systematic Survey
    Benyou Wang, Qianqian Xie, Jiahuan Pei, Zhihong Chen, Prayag Tiwari, Zhao Li, Jie Fu · 2023 · ACM Computing Surveys
93. Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions
    S. K. Jagatheesaperumal, Quoc-Viet Pham, Rukhsana Ruby, Zhaohui Yang, Chunmei Xu, Zhaoyang Zhang · 2022 · IEEE Open Journal of the Communications Society
94. Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review
    Singhal, Aditya, Neveditsin, Nikita, Tanveer, Hasnaat, Mago, Vijay · 2024 · JMIR Medical Informatics
95. Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending
    Miller Janny Ariza Garzón, Javier Arroyo, Antonio Caparrini, María Jesús Segovia Vargas · 2020 · IEEE Access
96. TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
    Augustus Odena, Ian Goodfellow · 2018 · arXiv (Cornell University)
97. A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations
    Karimi, Amir-Hossein, Barthe, Gilles, Schölkopf, Bernhard, Valera, Isabel · 2023 · ACM Computing Surveys
98. Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
    Liu, Yiming, Tan, Xinyu, Liang, Jie, Han, Hongwei, Xiang, Peng, Yan, Wensheng · 2023 · Advanced Functional Materials
99. Explainable artificial intelligence (XAI): Precepts, models, and opportunities for research in construction
    Peter E.D. Love, Weili Fang, Jane Matthews, Stuart R. Porter, Hanbin Luo, Lieyun Ding · 2023 · Advanced Engineering Informatics
100. The Psychological Well-Being of Content Moderators
     Steiger, Miriah, Bharucha, Timir J, Venkatagiri, Sukrit, Riedl, Martin J., Lease, Matthew · 2021 · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems