The mythos of model interpretability

Cited by (1,043)

Items 101–200 of 1,043

1. Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?
   Jacovi, Alon, Goldberg, Yoav · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
2. Explaining anomalies detected by autoencoders using Shapley Additive Explanations
   Antwarg, Liat, Miller, Ronnie Mindlin, Shapira, Bracha, Rokach, Lior · 2021 · Expert Systems with Applications
3. An Overview of Artificial Intelligence Ethics
   Changwu Huang, Zeqi Zhang, Bifei Mao, Xin Yao · 2022 · IEEE Transactions on Artificial Intelligence
4. Analysis Methods in Neural Language Processing: A Survey
   Yonatan Belinkov, James Robert Glass · 2019 · Transactions of the Association for Computational Linguistics
5. Opening the black box: interpretable machine learning for geneticists
   Azodi, Christina B., Tang, Jiliang, Shiu, Shin-Han · 2020 · Trends in Genetics
6. Attention is not Explanation
   Sarthak Jain, Byron Wallace · 2019 · arXiv (Cornell University)
7. Causal Interpretations of Black-Box Models
   Qingyuan Zhao, Trevor J. Hastie · 2019 · Journal of Business and Economic Statistics
8. Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
   Veale, Michael, Van Kleek, Max, Binns, Reuben · 2018 · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
9. Interpretability of deep learning models: A survey of results
   Supriyo Chakraborty, Richard Tomsett, Ramya Raghavendra, Daniel Harborne, Moustafa Alzantot, Federico Cerutti, Mani B. Srivastava, Alun D. Preece, Simon Julier, Raghuveer Rao et al. · 2017 · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
10. Explainable Deep Learning: A Field Guide for the Uninitiated
    Gabriëlle Ras, Ning Xie, Marcel A. J. van Gerven, Derek Doran · 2022 · Journal of Artificial Intelligence Research
11. Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
    Zhang, Zhibo, Hamadi, Hussam Al, Damiani, Ernesto, Yeun, Chan Yeob, Taher, Fatma · 2022 · IEEE Access
12. Improving palliative care with deep learning
    Anand Avati, Kenneth Jung, Stephanie M. Harman, Norman Lance Downing, A.Y. Ng, Nigam Haresh Shah · 2017 · IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
13. Transparency and trust in artificial intelligence systems
    Schmidt, Philipp, Biessmann, Felix, Teubner, Timm · 2020 · Journal of Decision Systems
14. Interpretable and Explainable Machine Learning for Materials Science and Chemistry
    Oviedo, Felipe, Ferres, Juan Lavista, Buonassisi, Tonio, Butler, Keith T. · 2022 · Accounts of Materials Research
15. AI-Based Digital Assistants: Opportunities, Threats, and Research Perspectives
    Maedche, Alexander, Legner, Christine, Benlian, Alexander, Berger, Benedikt, Gimpel, Henner, Hess, Thomas, Hinz, Oliver, Morana, Stefan, Söllner, Matthias · 2019 · Business & Information Systems Engineering
16. Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization
    Saurabh Ravindra Desai, Harish G. Ramaswamy · 2020 · IEEE Winter Conference on Applications of Computer Vision
17. Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems
    Thieme, Anja, Belgrave, Danielle, Doherty, Gavin · 2020 · ACM Transactions on Computer-Human Interaction
18. Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
    Buhrmester, Vanessa, Münch, David, Arens, Michael · 2021 · Machine Learning and Knowledge Extraction
19. Neural Machine Translation: A Review
    Felix Stahlberg · 2020 · Journal of Artificial Intelligence Research
20. Co-Designing a Real-Time Classroom Orchestration Tool to Support Teacher–AI Complementarity
    Kenneth Holstein, Bruce M. McLaren, Vincent Aleven · 2019 · Journal of Learning Analytics
21. Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence
    Zednik, Carlos · 2021 · Philosophy & Technology
22. Evaluating XAI: A comparison of rule-based and example-based explanations
    van der Waa, Jasper, Nieuwburg, Elisabeth, Cremers, Anita, Neerincx, Mark · 2021 · Artificial Intelligence
23. Deep learning in drug discovery: an integrative review and future challenges
    Heba Askr, Enas Elgeldawi, Heba Aboul-Ella, Y. Elshaier, Mamdouh M. Gomaa, A. Hassanien · 2022 · Artificial Intelligence Review
24. Integrating explanation and prediction in computational social science
    Hofman, Jake M., Watts, Duncan J., Athey, Susan, Garip, Filiz, Griffiths, Thomas L., Kleinberg, Jon, Margetts, Helen, Mullainathan, Sendhil, Salganik, Matthew J., Vazire, Simine et al. · 2021 · Nature
25. Explanations as Mechanisms for Supporting Algorithmic Transparency
    Rader, Emilee, Cotter, Kelley, Cho, Janghee · 2018 · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
26. Machine learning in concrete science: applications, challenges, and best practices
    Li, Zhanzhao, Yoon, Jinyoung, Zhang, Rui, Rajabipour, Farshad, Srubar III, Wil V., Dabo, Ismaila, Radlińska, Aleksandra · 2022 · npj Computational Materials
27. A Survey on Automated Fact-Checking
    Guo, Zhijiang, Schlichtkrull, Michael, Vlachos, Andreas · 2022 · Transactions of the Association for Computational Linguistics
28. A historical perspective of explainable Artificial Intelligence
    Roberto Confalonieri, Ludovik Çoba, Benedikt Wagner, Tarek R. Besold · 2020 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
29. Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
    Benidis, Konstantinos, Rangapuram, Syama Sundar, Flunkert, Valentin, Wang, Yuyang, Maddix, Danielle, Turkmen, Caner, Gasthaus, Jan, Bohlke-Schneider, Michael, Salinas, David, Stella, Lorenzo et al. · 2023 · ACM Computing Surveys
30. Viewpoint: Human-in-the-loop Artificial Intelligence
    Zanzotto, Fabio Massimo · 2019 · Journal of Artificial Intelligence Research
31. Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
    Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan · 2018 · arXiv (Cornell University)
32. Axes of a revolution: challenges and promises of big data in healthcare
    Shilo, Smadar, Rossman, Hagai, Segal, Eran · 2020 · Nature Medicine
33. Security and Privacy Challenges of Large Language Models: A Survey
    Das, Badhan Chandra, Amini, M. Hadi, Wu, Yanzhao · 2025 · ACM Computing Surveys
34. Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
    Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, Davide Maiorca, Giorgio Giacinto, Claudia Margot Eckert, Fabio Roli · 2018 · European Signal Processing Conference (EUSIPCO)
35. Multimodal sentiment analysis based on fusion methods: A survey
    Linan Zhu, Zhechao Zhu, Chenwei Zhang, Yifei Xu, Xiangjie Kong · 2023 · Information Fusion
36. Toward Human-Understandable, Explainable AI
    Hagras, Hani · 2018 · Computer
37. Interpreting CNNs via Decision Trees
    Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu · 2019 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
38. Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
    Nicolas Papernot, Patrick Drew McDaniel · 2018 · arXiv (Cornell University)
39. Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
    Dimiduk, Dennis M., Holm, Elizabeth A., Niezgoda, Stephen R. · 2018 · Integrating Materials and Manufacturing Innovation
40. Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
    Wang, Dakuo, Weisz, Justin D., Muller, Michael, Ram, Parikshit, Geyer, Werner, Dugan, Casey, Tausczik, Yla, Samulowitz, Horst, Gray, Alexander · 2019 · Proceedings of the ACM on Human-Computer Interaction
41. Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning
    Pedro Borlido, Jonathan Schmidt, Ahmad W. Huran, Fabien Tran, Miguel A. L. Marques, Silvana Botti · 2020 · npj Computational Materials
42. A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts
    Gesina Schwalbe, Bettina Finzel · 2023 · Data Mining and Knowledge Discovery
43. BERTology Meets Biology: Interpreting Attention in Protein Language Models
    Vig, Jesse, Madani, Ali, Varshney, Lav R., Xiong, Caiming, Socher, Richard, Rajani, Nazneen Fatema · 2020
44. A Review of Trustworthy and Explainable Artificial Intelligence (XAI)
    Chamola, Vinay, Hassija, Vikas, Sulthana, A Razia, Ghosh, Debshishu, Dhingra, Divyansh, Sikdar, Biplab · 2023 · IEEE Access
45. What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
    Doran, Derek, Schulz, Sarah, Besold, Tarek R. · 2017 · arXiv
46. Enchanted Determinism: Power without Responsibility in Artificial Intelligence
    Alexander Campolo, Kate Crawford · 2020 · Engaging Science Technology and Society
47. Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making
    Wang, Xinru, Yin, Ming · 2021 · 26th International Conference on Intelligent User Interfaces
48. What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
    Tonekaboni, Sana, Joshi, Shalmali, McCradden, Melissa D, Goldenberg, Anna · 2019 · arXiv
49. Explainable artificial intelligence (XAI) in finance: a systematic literature review
    Jurgita Černevičienė, Audrius Kabašinskas · 2024 · Artificial Intelligence Review
50. Pathologist-level interpretable whole-slide cancer diagnosis with deep learning
    Zhang, Zizhao, Chen, Pingjun, McGough, Mason, Xing, Fuyong, Wang, Chunbao, Bui, Marilyn, Xie, Yuanpu, Sapkota, Manish, Cui, Lei, Dhillon, Jasreman et al. · 2019 · Nature Machine Intelligence
51. Neural circuit policies enabling auditable autonomy
    Mathias Lechner, Ramin Hasani, Alexander Amini, Thomas A. Henzinger, Daniela L. Rus, Radu Grosu · 2020 · Nature Machine Intelligence
52. Machine and deep learning meet genome-scale metabolic modeling
    Guido Zampieri, Supreeta Vijayakumar, Elisabeth Yaneske, Claudio Angione · 2019 · PLoS Computational Biology
53. Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
    Livia Faes, Siegfried K. Wagner, Dun Jack Fu, Xiaoxuan Liu, Edward Korot, Joseph R. E. Ledsam, Trevor Back, Reena Chopra, Nikolas Pontikos, Christoph Kern et al. · 2019 · The Lancet Digital Health
54. Inherent Tradeoffs in the Fair Determination of Risk Scores
    Manish Raghavan · 2023 · ACM eBooks
55. Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare
    Hulsen, Tim · 2023 · AI
56. Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
    Brundage, Miles, Avin, Shahar, Wang, Jasmine, Belfield, Haydn, Krueger, Gretchen, Hadfield, Gillian, Khlaaf, Heidy, Yang, Jingying, Toner, Helen, Fong, Ruth et al. · 2020 · arXiv
57. State representation learning for control: An overview
    Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou, David Filliat · 2018 · Neural Networks
58. Deep materials informatics: Applications of deep learning in materials science
    Ankit Agrawal, Alok Choudhary · 2019 · MRS Communications
59. The relationship between trust in AI and trustworthy machine learning technologies
    Toreini, Ehsan, Aitken, Mhairi, Coopamootoo, Kovila, Elliott, Karen, Zelaya, Carlos Gonzalez, van Moorsel, Aad · 2020 · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
60. RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
    Kwon, Bum Chul, Choi, Min-Je, Kim, Joanne Taery, Choi, Edward, Kim, Young Bin, Kwon, Soonwook, Sun, Jimeng, Choo, Jaegul · 2019 · IEEE Transactions on Visualization and Computer Graphics
61. Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification
    Böhle, Moritz, Eitel, Fabian, Weygandt, Martin, Ritter, Kerstin · 2019 · Frontiers in Aging Neuroscience
62. Towards Reverse-Engineering Black-Box Neural Networks
    Oh, Seong Joon, Schiele, Bernt, Fritz, Mario · 2019 · Lecture Notes in Computer Science
63. Explaining a series of models by propagating Shapley values
    Hugh Chen, Scott Lundberg, Su‐In Lee · 2022 · Nature Communications
64. Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications
    Chou, Yu-Liang, Moreira, Catarina, Bruza, Peter, Ouyang, Chun, Jorge, Joaquim · 2022 · Information Fusion
65. AI in education: learner choice and fundamental rights
    Berendt, Bettina, Littlejohn, Allison, Blakemore, Mike · 2020 · Learning, Media and Technology
66. The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems
    Dellermann, Dominik, Calma, Adrian, Lipusch, Nikolaus, Weber, Thorsten, Weigel, Sascha, Ebel, Philipp · 2019 · Proceedings of the Annual Hawaii International Conference on System Sciences
67. Explainable Agents and Robots: Results from a Systematic Literature Review
    S. Anjomshoae, Amro Najjar, Davide Calvaresi, Kary Främling · 2019 · Adaptive Agents and Multi-Agent Systems
68. Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration
    Francesco Concas, Julien Mineraud, Eemil Lagerspetz, Samu Varjonen, Xiaoli Liu, Kai Puolamäki, Petteri Nurmi, Sasu Tarkoma · 2021 · ACM Transactions on Sensor Networks
69. To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts
    Nessrine Omrani, Giorgia Rivieccio, Ugo Fiore, Francesco Schiavone, Sergio Garcia Agreda · 2022 · Technological Forecasting and Social Change
70. A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges
    Jongseon Kim, Hyungjoon Kim, HyunGi Kim, Dongjun Lee, Sungroh Yoon · 2025 · Artificial Intelligence Review
71. Algorithmic recourse: From counterfactual explanations to interventions
    Karimi, Amir-Hossein, Schölkopf, Bernhard, Valera, Isabel · 2021 · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
72. Explainable Reinforcement Learning: A Survey
    Erika Puiutta, Eric MSP Veith · 2020 · Lecture notes in computer science
73. Relation between prognostics predictor evaluation metrics and local interpretability SHAP values
    Márcia Lourenço Baptista, Kai Goebel, Elsa M. P. Henriques · 2022 · Artificial Intelligence
74. Neural Prototype Trees for Interpretable Fine-grained Image Recognition
    Nauta, Meike, van Bree, Ron, Seifert, Christin · 2021 · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
75. Explainability fact sheets
    Kacper Sokol, Peter A. Flach · 2020 · ACM Conference on Fairness, Accountability, and Transparency (FAccT)
76. Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing
    Kevin Bauer, Moritz von Zahn, Oliver Fast Hinz · 2023 · Information Systems Research
77. Gradient-Based Attribution Methods
    Marco Ancona, Enea Ceolini, Cengiz Öztireli, Markus H. Groß · 2019 · Lecture notes in computer science
78. From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0
    Riccardo Rosati, Luca Romeo, Gianalberto Cecchini, Flavio Tonetto, Paolo Viti, Adriano Mancini, Emanuele Frontoni · 2022 · Journal of Intelligent Manufacturing
79. Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations
    Rong, Yao, Leemann, Tobias, Nguyen, Thai-Trang, Fiedler, Lisa, Qian, Peizhu, Unhelkar, Vaibhav, Seidel, Tina, Kasneci, Gjergji, Kasneci, Enkelejda · 2024 · IEEE Transactions on Pattern Analysis and Machine Intelligence
80. Four principles of explainable artificial intelligence
    Phillips, P Jonathon, Hahn, Carina A, Fontana, Peter C, Yates, Amy N, Greene, Kristen, Broniatowski, David A, Przybocki, Mark A · 2021
81. Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
    Cheng, Lu, Varshney, Kush R., Liu, Huan · 2021 · Journal of Artificial Intelligence Research
82. Artificial Discretion as a Tool of Governance: A Framework for Understanding the Impact of Artificial Intelligence on Public Administration
    Young, Matthew M, Bullock, Justin B, Lecy, Jesse D · 2019 · Perspectives on Public Management and Governance
83. Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
    Hase, Peter, Bansal, Mohit · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
84. How Much Position Information Do Convolutional Neural Networks Encode?
    Islam, Md Amirul, Jia, Sen, Bruce, Neil D. B. · 2020 · arXiv
85. Beware explanations from AI in health care
    Babic, Boris, Gerke, Sara, Evgeniou, Theodoros, Cohen, I. Glenn · 2021 · Science
86. To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems
    Amann, Julia, Vetter, Dennis, Blomberg, Stig Nikolaj, Christensen, Helle Collatz, Coffee, Megan, Gerke, Sara, Gilbert, Thomas K., Hagendorff, Thilo, Holm, Sune, Livne, Michelle et al. · 2022 · PLOS Digital Health
87. Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach
    Ehsan, Upol, Riedl, Mark O. · 2020 · Lecture Notes in Computer Science
88. Improving Robot Controller Transparency Through Autonomous Policy Explanation
    Bradley Hayes, Julie A. Shah · 2017 · IEEE/ACM International Conference on Human-Robot Interaction
89. Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction
    Melkamu Abay Mersha, Khang Nhứt Lâm, Joseph Wood, Ali K. AlShami, Jugal Kumar Kalita · 2024 · Neurocomputing
90. Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research
    AKM Bahalul Haque, A.K.M. Najmul Islam, Patrick Mikalef · 2022 · Technological Forecasting and Social Change
91. Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities
    Subash Neupane, Jesse Ables, William S. Anderson, Sudip Mittal, Shahram Rahimi, Ioana Banicescu, Maria A. Seale · 2022 · IEEE Access
92. Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability
    Ho, Sung Yang, Phua, Kimberly, Wong, Limsoon, Bin Goh, Wilson Wen · 2020 · Patterns
93. Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments
    Shijie Jiang, Yi Zheng, Chao Wang, Vladan Babovic · 2021 · Water Resources Research
94. A comparison among interpretative proposals for Random Forests
    Massimo Aria, Corrado Cuccurullo, Agostino Gnasso · 2021 · Machine Learning with Applications
95. Deep Reinforcement Learning
    Chong Li · 2019 · Chapman & Hall/CRC eBooks
96. Recent Advances in Trustworthy Explainable Artificial Intelligence: Status, Challenges, and Perspectives
    Atul Rawal, James McCoy, Danda B. Rawat, Brian M. Sadler, Robert St. Amant · 2021 · IEEE Transactions on Artificial Intelligence
97. Machine Learning for Clinical Outcome Prediction
    Farah E. Shamout, Tingting Zhu, David Andrew Clifton · 2020 · IEEE Reviews in Biomedical Engineering
98. Ethics & AI: A Systematic Review on Ethical Concerns and Related Strategies for Designing with AI in Healthcare
    Fan Li, Nick Ruijs, Yuan Lü · 2022 · AI
99. Causal interpretability for machine learning-problems, methods and evaluation
    Moraffah, Raha, Karami, Mansooreh, Guo, Ruocheng, Raglin, Adrienne, Liu, Huan · 2020 · ACM SIGKDD Explorations Newsletter
100. Explainable Reinforcement Learning: A Survey and Comparative Review
     Stephanie Milani, Nicholay Topin, Manuela Veloso, Fei Fang · 2023 · ACM Computing Surveys