The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.
Cited by (1,050)
Items 301–400 of 1,050
- General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning ModelsMolnar, 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
- Explainable artificial intelligence (XAI): Precepts, models, and opportunities for research in constructionPeter E.D. Love, Weili Fang, Jane Matthews, Stuart R. Porter, Hanbin Luo, Lieyun Ding · 2023 · Advanced Engineering Informatics
- The Psychological Well-Being of Content Moderators: The Emotional Labor of Commercial Moderation and Avenues for Improving SupportSteiger, Miriah, Bharucha, Timir J, Venkatagiri, Sukrit, Riedl, Martin J., Lease, Matthew · 2021 · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- A survey on artificial intelligence (ai) and explainable ai in air traffic management: current trends and development with future research trajectoryDegas, Augustin, Islam, Mir Riyanul, Hurter, Christophe, Barua, Shaibal, Rahman, Hamidur, Poudel, Minesh, Ruscio, Daniele, Ahmed, Mobyen Uddin, Begum, Shahina, Rahman, Md Aquif et al. · 2022 · Applied Sciences
- Explainable Artificial Intelligence (XAI) in Auditing: A Framework and Research NeedsChanyuan Zhang, Soohyun Cho, Miklos Antal Vasarhelyi · 2021 · International Journal of Accounting Information Systems
- Explainable Artificial Intelligence by Genetic Programming: A SurveyYi Mei, Qi Chen, Andrew Lensen, Bing Xue, Mengjie Zhang · 2022 · IEEE Transactions on Evolutionary Computation
- In the Land of the Blind, the One-Eyed Man Is King: Knowledge Brokerage in the Age of Learning AlgorithmsWaardenburg, Lauren, Huysman, Marleen, Sergeeva, Anastasia V. · 2022 · Organization Science
- Agent Transparency, Situation Awareness, Mental Workload, and Operator Performance: A Systematic Literature ReviewKoen van de Merwe, Steven Mallam, Salman Nazir · 2022 · Human Factors The Journal of the Human Factors and Ergonomics Society
- Interpretable and explainable machine learning: A methods‐centric overview with concrete examplesRičards Marcinkevičs, Julia Elisabeth Vogt · 2023 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
- Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning AlgorithmsHendrikus J. A. van Os, Lucas A. Ramos, Adam Hilbert, Matthijs van Leeuwen, Marianne A.A. van Walderveen, Nyika D. Kruyt, Diederik W.J. Dippel, Ewout Willem Steyerberg, Irene C. van der Schaaf, Hester Floor Lingsma et al. · 2018 · Frontiers in Neurology
- Explainable Deep Reinforcement Learning: State of the Art and ChallengesGeorge A. Vouros · 2022 · ACM Computing Surveys
- Evaluating saliency map explanations for convolutional neural networks: a user studyAlqaraawi, Ahmed, Schuessler, Martin, Weiß, Philipp, Costanza, Enrico, Berthouze, Nadia · 2020 · Proceedings of the 25th International Conference on Intelligent User Interfaces
- Applying deep learning to the newsvendor problemAfshin Oroojlooyjadid, Lawrence Snyder, Martin Takáč · 2019 · IISE Transactions
- How Explainability Contributes to Trust in AIFerrario, Andrea, Loi, Michele · 2022 · 2022 ACM Conference on Fairness Accountability and Transparency
- Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic ReviewQian Xu, Wenzhao Xie, Bolin Liao, Chao Hu, Lu Qin, Zhengzijin Yang, Huan Xiong, Lyu Yi, Yue Zhou, Aijing Luo · 2023 · Journal of Healthcare Engineering
- 'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions2018
- ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering BiasesPierre Stock, Moustapha Cissé · 2018 · Lecture notes in computer science
- Bridging the Gap Between AI and Explainability in the GDPR: Towards Trustworthiness-by-Design in Automated Decision-MakingRonan Hamon, H. Junklewitz, Ignacio Sanchez, Gianclaudio Malgieri, Paul De Hert · 2022 · IEEE Computational Intelligence Magazine
- Applications of Deep Learning to Neuro-Imaging TechniquesGuangming Zhu, Bin Jiang, Liz Tong, Yuan Xie, Greg Zaharchuk, Max Wintermark · 2019 · Frontiers in Neurology
- If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI TechniquesMark T. Keane, Eoin M. Kenny, Eoin D. Delaney, Barry Smyth · 2021 · International Joint Conference on Artificial Intelligence
- SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0Calabrese, Matteo, Cimmino, Martin, Fiume, Francesca, Manfrin, Martina, Romeo, Luca, Ceccacci, Silvia, Paolanti, Marina, Toscano, Giuseppe, Ciandrini, Giovanni, Carrotta, Alberto et al. · 2020 · Information
- Neural Machine Reading Comprehension: Methods and TrendsShanshan Liu, Xin Zhang, Sheng Zhang, Hui Wang, Weiming Zhang · 2019 · Applied Sciences
- Capturing the Style of Fake NewsPiotr Przybyła · 2020 · Proceedings of the AAAI Conference on Artificial Intelligence
- Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?Yash Raj Shrestha, Vivianna Fang He, Phanish Puranam, Georg von Krogh · 2020 · Organization Science
- Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A ReviewRocco Meli, Garrett M. Morris, Philip Charles Biggin · 2022 · Frontiers in Bioinformatics
- Programmatically Interpretable Reinforcement LearningAbhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri · 2018 · arXiv (Cornell University)
- The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?Jasmijn Bastings, Katja Filippova · 2020 · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP)
- A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident responseDipo Dunsin, Mohamed Chahine Ghanem, Karim Ouazzane, Vassil Vassilev · 2024 · Forensic Science International Digital Investigation
- Explainable AI in IndustryKrishna Gade, Sahin Cem Geyik, Krishnaram Kenthapadi, Varun Mithal, Ankur Taly · 2019 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
- Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologiesFeng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu · 2021 · Journal of Biomedical Informatics
- Explainable Artificial Intelligence for Neuroscience: Behavioral NeurostimulationJean‐Marc Fellous, Guillermo Sapiro, Andrew F. Rossi, Helen Mayberg, Michele Ferrante · 2019 · Frontiers in Neuroscience
- Classification of Explainable Artificial Intelligence Methods through Their Output FormatsGiulia Vilone, Luca Longo · 2021 · Machine Learning and Knowledge Extraction
- Ethical Dimensions of Visualization ResearchCorrell, Michael · 2019 · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
- Machine learning for nanoplasmonicsJean‐François Masson, John Simeon Biggins, Emilie Ringe · 2023 · Nature Nanotechnology
- Ethical machines: The human-centric use of artificial intelligenceLepri, Bruno, Oliver, Nuria, Pentland, Alex · 2021 · iScience
- An Empirical Study of Model-Agnostic Techniques for Defect Prediction ModelsJiarpakdee, Jirayus, Tantithamthavorn, Chakkrit Kla, Dam, Hoa Khanh, Grundy, John · 2022 · IEEE Transactions on Software Engineering
- Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)Mark T. Keane, Barry Smyth · 2020 · Lecture notes in computer science
- Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modelingDebaditya Chakraborty, Hakan Başağaoğlu, James R. Winterle · 2020 · Expert Systems with Applications
- Ethical Framework for Harnessing the Power of AI in Healthcare and BeyondNasir, Sidra, Khan, Rizwan Ahmed, Bai, Samita · 2024 · IEEE Access
- Using Machine Learning to Predict Complications in Pregnancy: A Systematic ReviewAyleen Bertini, Rodrigo Salas, Stéren Chabert, Luis Sobrevía, Fabián Pardo · 2022 · Frontiers in Bioengineering and Biotechnology
- Artificial intelligence and ethics within the food sector: Developing a common language for technology adoption across the supply chainLouise Manning, Steve Brewer, Peter J. Craigon, Jeremy Graham Frey, Anabel Gutiérrez, Naomi Jane Jacobs, Samantha Kanza, Samuel Munday, Justin Sacks, Simon Pearson · 2022 · Trends in Food Science & Technology
- Learning the PE Header, Malware Detection with Minimal Domain KnowledgeEdward Raff, Jared Sylvester, Charles Nicholas · 2017 · ACM Workshop on Artificial Intelligence and Security (AISec)
- A Yolo-Based Model for Breast Cancer Detection in MammogramsFrancesco Prinzi, Marco Insalaco, Alessia Angela Maria Orlando, Salvatore Gaglio, Salvatore Vitabile · 2023 · Cognitive Computation
- Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with ExplanationsChen, Valerie, Liao, Q. Vera, Wortman Vaughan, Jennifer, Bansal, Gagan · 2023 · Proceedings of the ACM on Human-Computer Interaction
- A Review of Explainable Deep Learning Cancer Detection Models in Medical ImagingMehmet Akif Gulum, Christopher M. Trombley, Mehmed M. Kantardzic · 2021 · Applied Sciences
- Multimodal Video Sentiment Analysis Using Deep Learning Approaches, a SurveySarah A. Abdu, Ahmed Hassan Yousef, Ashraf Salem · 2021 · Information Fusion
- Explaining deep neural networks: A survey on the global interpretation methodsRabia Saleem, Bo Yuan, Fatih Kurugöllü, Ashiq Anjum, Lu Liu · 2022 · Neurocomputing
- Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A SurveyMichela Prunella, Roberto Maria Scardigno, Domenico Buongiorno, Antonio Brunetti, Nicola Longo, Raffaele Carli, Mariagrazia Dotoli, Vitoantonio Bevilacqua · 2023 · IEEE Access
- Understanding artificial intelligence ethics and safetyDavid Leslie · 2019 · arXiv (Cornell University)
- It's complicated: the relationship between user trust, model accuracy and explanations in AIPapenmeier, Andrea, Kern, Dagmar, Englebienne, Gwenn, Seifert, Christin · 2022 · ACM Transactions on Computer-Human Interaction
- XAI Framework for Cardiovascular Disease Prediction Using Classification TechniquesPratiyush Guleria, Parvathaneni Naga Srinivasu, Shakeel Ahmed, Naif Almusallam, Fawaz Khaled Alarfaj · 2022 · Electronics
- Toward Explainable Artificial Intelligence for Precision PathologyFrederick Klauschen, Jonas Dippel, Philipp Keyl, Philipp Sebastian Jurmeister, Michael Ludwig Bockmayr, Andreas Möck, Oliver Buchstab, Maximilian Alber, Lukas Ruff, Grégoire Montavon et al. · 2023 · Annual Review of Pathology Mechanisms of Disease
- Quantum-inspired multimodal fusion for video sentiment analysisLi, Qiuchi, Gkoumas, Dimitris, Lioma, Christina, Melucci, Massimo · 2021 · Information Fusion
- Model agnostic generation of counterfactual explanations for moleculesWellawatte, Geemi P., Seshadri, Aditi, White, Andrew D. · 2022 · Chemical Science
- Using Machine Learning to Examine Impact of Type of Performance Indicator on Flexible Pavement Deterioration ModelingS. Madeh Piryonesi, Tamer E. El-Diraby · 2021 · Journal of Infrastructure Systems
- AI methods in materials design, discovery and manufacturing: A reviewIoannis Papadimitriou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Yiannis Kompatsiaris · 2024 · Computational Materials Science
- Exploring Two-Dimensional Materials Thermodynamic Stability via Machine LearningGabriel Ravanhani Schleder, Carlos Mera Acosta, A. Fazzio · 2019 · ACS Applied Materials & Interfaces
- Exploding the myths: An introduction to artificial neural networks for prediction and forecastingHolger Robert Maier, Stefano Galelli, Saman Razavi, Andrea Francesco Castelletti, Andrea Emilio Rizzoli, Ioannis N. Athanasiadis, Miquel Sànchez–Marrè, Marco Acutis, Wenyan Wu, Greer B. Humphrey · 2023 · Environmental Modelling & Software
- Scanpath modeling and classification with hidden Markov modelsAntoine Coutrot, Janet H. Hsiao, Antoni B. Chan · 2017 · Behavior Research Methods
- Explainable software analyticsHoa Khanh Dam, Truyen Tran, Aditya Ghose · 2018 · IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)
- Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferencesMathias Kraus, Stefan Feuerriegel · 2019 · Decision Support Systems
- Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact DescriptionsHai Ye, Xin Jiang, Zhunchen Luo, Wenhan Chao · 2018 · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)
- Critical appraisal of artificial intelligence-based prediction models for cardiovascular diseaseM. van Smeden, Georg Heinze, B. van Calster, F. Asselbergs, P. Vardas, N. Bruining, P. de Jaegere, Jason H. Moore, Spiros C. Denaxas, Anne-Laure Boulesteix et al. · 2022 · European Heart Journal
- A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurityVincent Zibi Mohale, Ibidun Christiana Obagbuwa · 2025 · Frontiers in Artificial Intelligence
- Exploring Interpretable LSTM Neural Networks over Multi-Variable DataTian Guo, Tao Lin, Nino Antulov-Fantulin · 2019 · arXiv (Cornell University)
- I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AIChromik, Michael, Eiband, Malin, Buchner, Felicitas, Krüger, Adrian, Butz, Andreas · 2021 · 26th International Conference on Intelligent User Interfaces
- Learning to Faithfully Rationalize by ConstructionSarthak Jain, Sarah Wiegreffe, Yuval Pinter, Byron Wallace · 2020 · Proceedings of the conference - Association for Computational Linguistics. Meeting
- Implementing machine learning in medicineAmol A. Verma, Joshua Murray, Russell Greiner, Joseph Cohen, Kaveh G Shojania, Marzyeh Ghassemi, Sharon Elizabeth Straus, Chloé Pou-Prom, Muhammad Mamdani · 2021 · Canadian Medical Association Journal
- Automatic sleep staging of EEG signals: recent development, challenges, and future directionsHuy Phan, Kaare B. Mikkelsen · 2022 · Physiological Measurement
- DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learningQiyuan Tian, Berkin Bilgiç, Qiuyun Fan, Congyu Liao, Chanon Ngamsombat, Yuxin Hu, Thomas Witzel, Kawin Setsompop, Jon̈athan R. Polimeni, Susie Y. Huang · 2020 · NeuroImage
- Contemporary Symbolic Regression Methods and their Relative PerformanceWilliam La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Jin, Ying, Michael Kommenda, Jason H. Moore · 2021 · PubMed
- Four Principles of Explainable Artificial IntelligencePhillips, P. Jonathon, Hahn, Carina A., Fontana, Peter C., Broniatowski, David A., Przybocki, Mark A. · 2020
- Improving Model-Based Genetic Programming for Symbolic Regression of Small ExpressionsM. Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman · 2020 · Evolutionary Computation
- Artificial intelligence explainability: the technical and ethical dimensionsMcDermid, John A., Jia, Yan, Porter, Zoe, Habli, Ibrahim · 2021 · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource managementMarkus Langer, Cornelius J. König · 2021 · Human Resource Management Review
- Using machine learning as a surrogate model for agent-based simulationsClaudio Angione, Eric Silverman, Elisabeth Yaneske · 2022 · PLoS ONE
- A comprehensive review on financial explainable AIWei Jie Yeo, Wihan van der Heever, Rui Mao, Erik Cambria, Ranjan Satapathy, Gianmarco Mengaldo · 2025 · Artificial Intelligence Review
- Explainable Artificial Intelligence for Tabular Data: A SurveySahakyan, Maria, Aung, Zeyar, Rahwan, Talal · 2021 · IEEE Access
- The explainability paradox: Challenges for xAI in digital pathologyEvans, Theodore, Retzlaff, Carl Orge, Geißler, Christian, Kargl, Michaela, Plass, Markus, Müller, Heimo, Kiehl, Tim-Rasmus, Zerbe, Norman, Holzinger, Andreas · 2022 · Future Generation Computer Systems
- Interpretable Machine Learning: A brief survey from the predictive maintenance perspectiveSimon Vollert, Martin Atzmueller, Andreas Theissler · 2021 · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
- Applying Deep Learning to Medical Imaging: A ReviewHuan Huan Zhang, Yufei Qie · 2023 · Applied Sciences
- Eight Things to Know about Large Language ModelsSamuel R. Bowman · 2024 · Critical AI
- Can we do better explanations? A proposal of user-centered explainable AIMireia Ribera, Àgata Lapedriza · 2019 · IUI Workshops
- A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future DirectionsTalal A. A. Abdullah, Mohd Soperi Mohd Zahid, Waleed Ali · 2021 · Symmetry
- Explainability and Interpretability in Electric Load Forecasting Using Machine Learning Techniques – A ReviewLukas Baur, Konstantin Ditschuneit, Maximilian Schambach, Can Kaymakci, Thomas Wollmann, Alexander Sauer · 2024 · Energy and AI
- Toward Explainable Deep Neural Network Based Anomaly DetectionKasun Amarasinghe, Kevin Kenney, Milos Manic · 2018 · Conference on Human System Interactions
- The Impact of Feature Importance Methods on the Interpretation of Defect ClassifiersGopi Krishnan Rajbahadur, Shaowei Wang, Gustavo A. Oliva, Yasutaka Kamei, Ahmed E. Hassan · 2021 · IEEE Transactions on Software Engineering
- AI Anxiety: a comprehensive analysis of psychological factors and interventionsJeff J. H. Kim, Jun Young Soh, Shrinidhi Kadkol, Itay Solomon, Hyelin Yeh, Adith V. Srivatsa, George Ronald Nahass, Jeong Yun Choi, Sophie Lee, Theresa Nyugen et al. · 2025 · AI and Ethics
- Predicting the mechanical response of oligocrystals with deep learningAri Frankel, Reese E. Jones, Coleman N. Alleman, Jeremy Alan Templeton · 2019 · Computational Materials Science
- A Survey on Explainable Anomaly DetectionLi, Zhong, Zhu, Yuxuan, Van Leeuwen, Matthijs · 2024 · ACM Transactions on Knowledge Discovery from Data
- Machine Learning Uncertainty as a Design Material: A Post-Phenomenological InquiryBenjamin, Jesse Josua, Berger, Arne, Merrill, Nick, Pierce, James · 2021 · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning ResearchJennifer Wortman Vaughan · 2018 · Journal of Machine Learning Research
- Evaluation of post-hoc interpretability methods in time-series classificationHugues Turbé, Mina Bjelogrlic, Christian Lovis, Gianmarco Mengaldo · 2023 · Nature Machine Intelligence
- How Cognitive Biases Affect XAI-assisted Decision-making: A Systematic ReviewBertrand, Astrid, Belloum, Rafik, Eagan, James R., Maxwell, Winston · 2022 · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society
- Fairness in Machine Learning: A SurveyCaton, Simon, Haas, Christian · 2020 · arXiv
- Towards Faithful Model Explanation in NLP: A SurveyQing Lyu, Marianna Apidianaki, Chris Callison-Burch · 2024 · Computational Linguistics
- Co-design of human-centered, explainable AI for clinical decision supportPanigutti, Cecilia, Beretta, Andrea, Fadda, Daniele, Giannotti, Fosca, Pedreschi, Dino, Perotti, Alan, Rinzivillo, Salvatore · 2023 · ACM Transactions on Interactive Intelligent Systems
- What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP ModelsFahim Dalvi, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, Anthony Bau, James Robert Glass · 2019 · Proceedings of the AAAI Conference on Artificial Intelligence
- From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into PracticesJessica Morley, Luciano Floridi, Libby Kinsey, Anat Elhalal · 2021 · Philosophical studies series
- CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination TherapyBo Yuan, Ciyue Shen, Augustin Luna, Anil Korkut, Debora S. Marks, John Ingraham, Chris Sander · 2020 · Cell Systems