Artículos de revistas sobre el tema "Counterfactual explanations"
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Sia, Suzanna, Anton Belyy, Amjad Almahairi, Madian Khabsa, Luke Zettlemoyer y Lambert Mathias. "Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 8 (26 de junio de 2023): 9837–45. http://dx.doi.org/10.1609/aaai.v37i8.26174.
Texto completoAsher, Nicholas, Lucas De Lara, Soumya Paul y Chris Russell. "Counterfactual Models for Fair and Adequate Explanations". Machine Learning and Knowledge Extraction 4, n.º 2 (31 de marzo de 2022): 316–49. http://dx.doi.org/10.3390/make4020014.
Texto completoVanNostrand, Peter M., Huayi Zhang, Dennis M. Hofmann y Elke A. Rundensteiner. "FACET: Robust Counterfactual Explanation Analytics". Proceedings of the ACM on Management of Data 1, n.º 4 (8 de diciembre de 2023): 1–27. http://dx.doi.org/10.1145/3626729.
Texto completoKenny, Eoin M. y Mark T. Keane. "On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 13 (18 de mayo de 2021): 11575–85. http://dx.doi.org/10.1609/aaai.v35i13.17377.
Texto completoLeofante, Francesco y Nico Potyka. "Promoting Counterfactual Robustness through Diversity". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 19 (24 de marzo de 2024): 21322–30. http://dx.doi.org/10.1609/aaai.v38i19.30127.
Texto completoDelaney, Eoin, Arjun Pakrashi, Derek Greene y Mark T. Keane. "Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ (Abstract Reprint)". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 20 (24 de marzo de 2024): 22696. http://dx.doi.org/10.1609/aaai.v38i20.30596.
Texto completoBaron, Sam, Mark Colyvan y David Ripley. "A Counterfactual Approach to Explanation in Mathematics". Philosophia Mathematica 28, n.º 1 (2 de diciembre de 2019): 1–34. http://dx.doi.org/10.1093/philmat/nkz023.
Texto completoSchleich, Maximilian, Zixuan Geng, Yihong Zhang y Dan Suciu. "GeCo". Proceedings of the VLDB Endowment 14, n.º 9 (mayo de 2021): 1681–93. http://dx.doi.org/10.14778/3461535.3461555.
Texto completoBarzekar, Hosein y Susan McRoy. "Achievable Minimally-Contrastive Counterfactual Explanations". Machine Learning and Knowledge Extraction 5, n.º 3 (3 de agosto de 2023): 922–36. http://dx.doi.org/10.3390/make5030048.
Texto completoChapman-Rounds, Matt, Umang Bhatt, Erik Pazos, Marc-Andre Schulz y Konstantinos Georgatzis. "FIMAP: Feature Importance by Minimal Adversarial Perturbation". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 13 (18 de mayo de 2021): 11433–41. http://dx.doi.org/10.1609/aaai.v35i13.17362.
Texto completoLabaien Soto, Jokin, Ekhi Zugasti Uriguen y Xabier De Carlos Garcia. "Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders". Applied Sciences 13, n.º 5 (24 de febrero de 2023): 2912. http://dx.doi.org/10.3390/app13052912.
Texto completoBaron, Sam. "Counterfactual Scheming". Mind 129, n.º 514 (1 de abril de 2019): 535–62. http://dx.doi.org/10.1093/mind/fzz008.
Texto completoHe, Ming, Boyang An, Jiwen Wang y Hao Wen. "CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation". Applied Sciences 13, n.º 20 (11 de octubre de 2023): 11176. http://dx.doi.org/10.3390/app132011176.
Texto completoGeng, Zixuan, Maximilian Schleich y Dan Suciu. "Computing Rule-Based Explanations by Leveraging Counterfactuals". Proceedings of the VLDB Endowment 16, n.º 3 (noviembre de 2022): 420–32. http://dx.doi.org/10.14778/3570690.3570693.
Texto completoFernandes, Alison. "Back to the Present: How Not to Use Counterfactuals to Explain Causal Asymmetry". Philosophies 7, n.º 2 (9 de abril de 2022): 43. http://dx.doi.org/10.3390/philosophies7020043.
Texto completoAryal, Saugat. "Semi-factual Explanations in AI". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 21 (24 de marzo de 2024): 23379–80. http://dx.doi.org/10.1609/aaai.v38i21.30390.
Texto completoFernández-Loría, Carlos, Foster Provost y Xintian Han. "Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach". MIS Quarterly 45, n.º 3 (1 de septiembre de 2022): 1635–60. http://dx.doi.org/10.25300/misq/2022/16749.
Texto completoPrado-Romero, Mario Alfonso, Bardh Prenkaj y Giovanni Stilo. "Robust Stochastic Graph Generator for Counterfactual Explanations". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 19 (24 de marzo de 2024): 21518–26. http://dx.doi.org/10.1609/aaai.v38i19.30149.
Texto completoVries, Katja de. "Transparent Dreams (Are Made of This): Counterfactuals as Transparency Tools in ADM". Critical Analysis of Law 8, n.º 1 (2 de abril de 2021): 121–38. http://dx.doi.org/10.33137/cal.v8i1.36283.
Texto completoSokol, Kacper y Peter Flach. "Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 10035–36. http://dx.doi.org/10.1609/aaai.v33i01.330110035.
Texto completoCong, Zicun, Lingyang Chu, Yu Yang y Jian Pei. "Comprehensible counterfactual explanation on Kolmogorov-Smirnov test". Proceedings of the VLDB Endowment 14, n.º 9 (mayo de 2021): 1583–96. http://dx.doi.org/10.14778/3461535.3461546.
Texto completoLe, Thao, Tim Miller, Ronal Singh y Liz Sonenberg. "Explaining Model Confidence Using Counterfactuals". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 10 (26 de junio de 2023): 11856–64. http://dx.doi.org/10.1609/aaai.v37i10.26399.
Texto completoSunstein, Cass R. "Historical Explanations Always Involve Counterfactual History". Journal of the Philosophy of History 10, n.º 3 (17 de noviembre de 2016): 433–40. http://dx.doi.org/10.1163/18722636-12341345.
Texto completoMadumal, Prashan, Tim Miller, Liz Sonenberg y Frank Vetere. "Explainable Reinforcement Learning through a Causal Lens". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 03 (3 de abril de 2020): 2493–500. http://dx.doi.org/10.1609/aaai.v34i03.5631.
Texto completoLai, Chengen, Shengli Song, Shiqi Meng, Jingyang Li, Sitong Yan y Guangneng Hu. "Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQA". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 3 (24 de marzo de 2024): 2849–57. http://dx.doi.org/10.1609/aaai.v38i3.28065.
Texto completoAmitai, Yotam, Yael Septon y Ofra Amir. "Explaining Reinforcement Learning Agents through Counterfactual Action Outcomes". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 9 (24 de marzo de 2024): 10003–11. http://dx.doi.org/10.1609/aaai.v38i9.28863.
Texto completoMcEleney, Alice y Ruth M. J. Byrne. "Spontaneous counterfactual thoughts and causal explanations". Thinking & Reasoning 12, n.º 2 (mayo de 2006): 235–55. http://dx.doi.org/10.1080/13546780500317897.
Texto completoLucic, Ana, Harrie Oosterhuis, Hinda Haned y Maarten de Rijke. "FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 5 (28 de junio de 2022): 5313–22. http://dx.doi.org/10.1609/aaai.v36i5.20468.
Texto completoLee, Min Hun y Chong Jun Chew. "Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making". Proceedings of the ACM on Human-Computer Interaction 7, CSCW2 (28 de septiembre de 2023): 1–22. http://dx.doi.org/10.1145/3610218.
Texto completoCarreira-Perpiñán, Miguel Á. y Suryabhan Singh Hada. "Counterfactual Explanations for Oblique Decision Trees:Exact, Efficient Algorithms". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 8 (18 de mayo de 2021): 6903–11. http://dx.doi.org/10.1609/aaai.v35i8.16851.
Texto completoAltmeyer, Patrick, Mojtaba Farmanbar, Arie Van Deursen y Cynthia C. S. Liem. "Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 10 (24 de marzo de 2024): 10829–37. http://dx.doi.org/10.1609/aaai.v38i10.28956.
Texto completoGulshad, Sadaf y Arnold Smeulders. "Counterfactual attribute-based visual explanations for classification". International Journal of Multimedia Information Retrieval 10, n.º 2 (18 de abril de 2021): 127–40. http://dx.doi.org/10.1007/s13735-021-00208-3.
Texto completoYacoby, Yaniv, Ben Green, Christopher L. Griffin Jr. y Finale Doshi-Velez. "“If it didn’t happen, why would I change my decision?”: How Judges Respond to Counterfactual Explanations for the Public Safety Assessment". Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 10, n.º 1 (14 de octubre de 2022): 219–30. http://dx.doi.org/10.1609/hcomp.v10i1.22001.
Texto completoVirmajoki, Veli. "Frameworks in Historiography: Explanation, Scenarios, and Futures". Journal of the Philosophy of History 17, n.º 2 (3 de julio de 2023): 288–309. http://dx.doi.org/10.1163/18722636-12341501.
Texto completoLey, Dan, Umang Bhatt y Adrian Weller. "Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junio de 2022): 7390–98. http://dx.doi.org/10.1609/aaai.v36i7.20702.
Texto completoWellawatte, Geemi P., Aditi Seshadri y Andrew D. White. "Model agnostic generation of counterfactual explanations for molecules". Chemical Science 13, n.º 13 (2022): 3697–705. http://dx.doi.org/10.1039/d1sc05259d.
Texto completoPiccione, A., J. W. Berkery, S. A. Sabbagh y Y. Andreopoulos. "Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanations". Nuclear Fusion 62, n.º 3 (18 de enero de 2022): 036002. http://dx.doi.org/10.1088/1741-4326/ac44af.
Texto completoJiang, Junqi, Francesco Leofante, Antonio Rago y Francesca Toni. "Formalising the Robustness of Counterfactual Explanations for Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 12 (26 de junio de 2023): 14901–9. http://dx.doi.org/10.1609/aaai.v37i12.26740.
Texto completode Oliveira, Raphael Mazzine Barbosa y David Martens. "A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data". Applied Sciences 11, n.º 16 (7 de agosto de 2021): 7274. http://dx.doi.org/10.3390/app11167274.
Texto completoAkula, Arjun, Shuai Wang y Song-Chun Zhu. "CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 03 (3 de abril de 2020): 2594–601. http://dx.doi.org/10.1609/aaai.v34i03.5643.
Texto completoReutlinger, Alexander. "Does the counterfactual theory of explanation apply to non-causal explanations in metaphysics?" European Journal for Philosophy of Science 7, n.º 2 (19 de agosto de 2016): 239–56. http://dx.doi.org/10.1007/s13194-016-0155-z.
Texto completoCarreira-Perpinan, Miguel Á. y Suryabhan Singh Hada. "Very Fast, Approximate Counterfactual Explanations for Decision Forests". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junio de 2023): 6935–43. http://dx.doi.org/10.1609/aaai.v37i6.25848.
Texto completoChalyi, Serhii, Volodymyr Leshchynskyi y Irina Leshchynska. "COUNTERFACTUAL TEMPORAL MODEL OF CAUSAL RELATIONSHIPS FOR CONSTRUCTING EXPLANATIONS IN INTELLIGENT SYSTEMS". Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, n.º 2 (6) (28 de diciembre de 2021): 41–46. http://dx.doi.org/10.20998/2079-0023.2021.02.07.
Texto completoAdmassu, Tsehay. "Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection". Proceedings of Engineering and Technology Innovation 23 (1 de enero de 2023): 48–59. http://dx.doi.org/10.46604/peti.2023.10101.
Texto completoZahedi, Zahra, Sailik Sengupta y Subbarao Kambhampati. "‘Why Didn’t You Allocate This Task to Them?’ Negotiation-Aware Task Allocation and Contrastive Explanation Generation". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 9 (24 de marzo de 2024): 10243–51. http://dx.doi.org/10.1609/aaai.v38i9.28890.
Texto completoNolan, Daniel. "The Possibilities of History". Journal of the Philosophy of History 10, n.º 3 (17 de noviembre de 2016): 441–56. http://dx.doi.org/10.1163/18722636-12341346.
Texto completode Brito Duarte, Regina, Filipa Correia, Patrícia Arriaga y Ana Paiva. "AI Trust: Can Explainable AI Enhance Warranted Trust?" Human Behavior and Emerging Technologies 2023 (31 de octubre de 2023): 1–12. http://dx.doi.org/10.1155/2023/4637678.
Texto completoFreiesleben, Timo. "The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples". Minds and Machines 32, n.º 1 (30 de octubre de 2021): 77–109. http://dx.doi.org/10.1007/s11023-021-09580-9.
Texto completoGuidotti, Riccardo, Anna Monreale, Fosca Giannotti, Dino Pedreschi, Salvatore Ruggieri y Franco Turini. "Factual and Counterfactual Explanations for Black Box Decision Making". IEEE Intelligent Systems 34, n.º 6 (1 de noviembre de 2019): 14–23. http://dx.doi.org/10.1109/mis.2019.2957223.
Texto completoWang, Xiangmeng, Qian Li, Dianer Yu, Qing Li y Guandong Xu. "Counterfactual Explanation for Fairness in Recommendation". ACM Transactions on Information Systems, 29 de enero de 2024. http://dx.doi.org/10.1145/3643670.
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