Artykuły w czasopismach na temat „Counterfactual explanations”
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Sia, Suzanna, Anton Belyy, Amjad Almahairi, Madian Khabsa, Luke Zettlemoyer i Lambert Mathias. "Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 8 (26.06.2023): 9837–45. http://dx.doi.org/10.1609/aaai.v37i8.26174.
Pełny tekst źródłaAsher, Nicholas, Lucas De Lara, Soumya Paul i Chris Russell. "Counterfactual Models for Fair and Adequate Explanations". Machine Learning and Knowledge Extraction 4, nr 2 (31.03.2022): 316–49. http://dx.doi.org/10.3390/make4020014.
Pełny tekst źródłaVanNostrand, Peter M., Huayi Zhang, Dennis M. Hofmann i Elke A. Rundensteiner. "FACET: Robust Counterfactual Explanation Analytics". Proceedings of the ACM on Management of Data 1, nr 4 (8.12.2023): 1–27. http://dx.doi.org/10.1145/3626729.
Pełny tekst źródłaKenny, Eoin M., i Mark T. Keane. "On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 13 (18.05.2021): 11575–85. http://dx.doi.org/10.1609/aaai.v35i13.17377.
Pełny tekst źródłaLeofante, Francesco, i Nico Potyka. "Promoting Counterfactual Robustness through Diversity". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 19 (24.03.2024): 21322–30. http://dx.doi.org/10.1609/aaai.v38i19.30127.
Pełny tekst źródłaDelaney, Eoin, Arjun Pakrashi, Derek Greene i 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, nr 20 (24.03.2024): 22696. http://dx.doi.org/10.1609/aaai.v38i20.30596.
Pełny tekst źródłaBaron, Sam, Mark Colyvan i David Ripley. "A Counterfactual Approach to Explanation in Mathematics". Philosophia Mathematica 28, nr 1 (2.12.2019): 1–34. http://dx.doi.org/10.1093/philmat/nkz023.
Pełny tekst źródłaSchleich, Maximilian, Zixuan Geng, Yihong Zhang i Dan Suciu. "GeCo". Proceedings of the VLDB Endowment 14, nr 9 (maj 2021): 1681–93. http://dx.doi.org/10.14778/3461535.3461555.
Pełny tekst źródłaBarzekar, Hosein, i Susan McRoy. "Achievable Minimally-Contrastive Counterfactual Explanations". Machine Learning and Knowledge Extraction 5, nr 3 (3.08.2023): 922–36. http://dx.doi.org/10.3390/make5030048.
Pełny tekst źródłaChapman-Rounds, Matt, Umang Bhatt, Erik Pazos, Marc-Andre Schulz i Konstantinos Georgatzis. "FIMAP: Feature Importance by Minimal Adversarial Perturbation". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 13 (18.05.2021): 11433–41. http://dx.doi.org/10.1609/aaai.v35i13.17362.
Pełny tekst źródłaLabaien Soto, Jokin, Ekhi Zugasti Uriguen i Xabier De Carlos Garcia. "Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders". Applied Sciences 13, nr 5 (24.02.2023): 2912. http://dx.doi.org/10.3390/app13052912.
Pełny tekst źródłaBaron, Sam. "Counterfactual Scheming". Mind 129, nr 514 (1.04.2019): 535–62. http://dx.doi.org/10.1093/mind/fzz008.
Pełny tekst źródłaHe, Ming, Boyang An, Jiwen Wang i Hao Wen. "CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation". Applied Sciences 13, nr 20 (11.10.2023): 11176. http://dx.doi.org/10.3390/app132011176.
Pełny tekst źródłaGeng, Zixuan, Maximilian Schleich i Dan Suciu. "Computing Rule-Based Explanations by Leveraging Counterfactuals". Proceedings of the VLDB Endowment 16, nr 3 (listopad 2022): 420–32. http://dx.doi.org/10.14778/3570690.3570693.
Pełny tekst źródłaFernandes, Alison. "Back to the Present: How Not to Use Counterfactuals to Explain Causal Asymmetry". Philosophies 7, nr 2 (9.04.2022): 43. http://dx.doi.org/10.3390/philosophies7020043.
Pełny tekst źródłaAryal, Saugat. "Semi-factual Explanations in AI". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 21 (24.03.2024): 23379–80. http://dx.doi.org/10.1609/aaai.v38i21.30390.
Pełny tekst źródłaFernández-Loría, Carlos, Foster Provost i Xintian Han. "Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach". MIS Quarterly 45, nr 3 (1.09.2022): 1635–60. http://dx.doi.org/10.25300/misq/2022/16749.
Pełny tekst źródłaPrado-Romero, Mario Alfonso, Bardh Prenkaj i Giovanni Stilo. "Robust Stochastic Graph Generator for Counterfactual Explanations". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 19 (24.03.2024): 21518–26. http://dx.doi.org/10.1609/aaai.v38i19.30149.
Pełny tekst źródłaVries, Katja de. "Transparent Dreams (Are Made of This): Counterfactuals as Transparency Tools in ADM". Critical Analysis of Law 8, nr 1 (2.04.2021): 121–38. http://dx.doi.org/10.33137/cal.v8i1.36283.
Pełny tekst źródłaSokol, Kacper, i Peter Flach. "Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 10035–36. http://dx.doi.org/10.1609/aaai.v33i01.330110035.
Pełny tekst źródłaCong, Zicun, Lingyang Chu, Yu Yang i Jian Pei. "Comprehensible counterfactual explanation on Kolmogorov-Smirnov test". Proceedings of the VLDB Endowment 14, nr 9 (maj 2021): 1583–96. http://dx.doi.org/10.14778/3461535.3461546.
Pełny tekst źródłaLe, Thao, Tim Miller, Ronal Singh i Liz Sonenberg. "Explaining Model Confidence Using Counterfactuals". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 10 (26.06.2023): 11856–64. http://dx.doi.org/10.1609/aaai.v37i10.26399.
Pełny tekst źródłaSunstein, Cass R. "Historical Explanations Always Involve Counterfactual History". Journal of the Philosophy of History 10, nr 3 (17.11.2016): 433–40. http://dx.doi.org/10.1163/18722636-12341345.
Pełny tekst źródłaMadumal, Prashan, Tim Miller, Liz Sonenberg i Frank Vetere. "Explainable Reinforcement Learning through a Causal Lens". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 03 (3.04.2020): 2493–500. http://dx.doi.org/10.1609/aaai.v34i03.5631.
Pełny tekst źródłaLai, Chengen, Shengli Song, Shiqi Meng, Jingyang Li, Sitong Yan i Guangneng Hu. "Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQA". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 3 (24.03.2024): 2849–57. http://dx.doi.org/10.1609/aaai.v38i3.28065.
Pełny tekst źródłaAmitai, Yotam, Yael Septon i Ofra Amir. "Explaining Reinforcement Learning Agents through Counterfactual Action Outcomes". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 9 (24.03.2024): 10003–11. http://dx.doi.org/10.1609/aaai.v38i9.28863.
Pełny tekst źródłaMcEleney, Alice, i Ruth M. J. Byrne. "Spontaneous counterfactual thoughts and causal explanations". Thinking & Reasoning 12, nr 2 (maj 2006): 235–55. http://dx.doi.org/10.1080/13546780500317897.
Pełny tekst źródłaLucic, Ana, Harrie Oosterhuis, Hinda Haned i Maarten de Rijke. "FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 5 (28.06.2022): 5313–22. http://dx.doi.org/10.1609/aaai.v36i5.20468.
Pełny tekst źródłaLee, Min Hun, i 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 (28.09.2023): 1–22. http://dx.doi.org/10.1145/3610218.
Pełny tekst źródłaCarreira-Perpiñán, Miguel Á., i Suryabhan Singh Hada. "Counterfactual Explanations for Oblique Decision Trees:Exact, Efficient Algorithms". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 8 (18.05.2021): 6903–11. http://dx.doi.org/10.1609/aaai.v35i8.16851.
Pełny tekst źródłaAltmeyer, Patrick, Mojtaba Farmanbar, Arie Van Deursen i Cynthia C. S. Liem. "Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 10 (24.03.2024): 10829–37. http://dx.doi.org/10.1609/aaai.v38i10.28956.
Pełny tekst źródłaGulshad, Sadaf, i Arnold Smeulders. "Counterfactual attribute-based visual explanations for classification". International Journal of Multimedia Information Retrieval 10, nr 2 (18.04.2021): 127–40. http://dx.doi.org/10.1007/s13735-021-00208-3.
Pełny tekst źródłaYacoby, Yaniv, Ben Green, Christopher L. Griffin Jr. i 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, nr 1 (14.10.2022): 219–30. http://dx.doi.org/10.1609/hcomp.v10i1.22001.
Pełny tekst źródłaVirmajoki, Veli. "Frameworks in Historiography: Explanation, Scenarios, and Futures". Journal of the Philosophy of History 17, nr 2 (3.07.2023): 288–309. http://dx.doi.org/10.1163/18722636-12341501.
Pełny tekst źródłaLey, Dan, Umang Bhatt i Adrian Weller. "Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 7 (28.06.2022): 7390–98. http://dx.doi.org/10.1609/aaai.v36i7.20702.
Pełny tekst źródłaWellawatte, Geemi P., Aditi Seshadri i Andrew D. White. "Model agnostic generation of counterfactual explanations for molecules". Chemical Science 13, nr 13 (2022): 3697–705. http://dx.doi.org/10.1039/d1sc05259d.
Pełny tekst źródłaPiccione, A., J. W. Berkery, S. A. Sabbagh i Y. Andreopoulos. "Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanations". Nuclear Fusion 62, nr 3 (18.01.2022): 036002. http://dx.doi.org/10.1088/1741-4326/ac44af.
Pełny tekst źródłaJiang, Junqi, Francesco Leofante, Antonio Rago i Francesca Toni. "Formalising the Robustness of Counterfactual Explanations for Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 12 (26.06.2023): 14901–9. http://dx.doi.org/10.1609/aaai.v37i12.26740.
Pełny tekst źródłade Oliveira, Raphael Mazzine Barbosa, i David Martens. "A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data". Applied Sciences 11, nr 16 (7.08.2021): 7274. http://dx.doi.org/10.3390/app11167274.
Pełny tekst źródłaAkula, Arjun, Shuai Wang i Song-Chun Zhu. "CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 03 (3.04.2020): 2594–601. http://dx.doi.org/10.1609/aaai.v34i03.5643.
Pełny tekst źródłaReutlinger, Alexander. "Does the counterfactual theory of explanation apply to non-causal explanations in metaphysics?" European Journal for Philosophy of Science 7, nr 2 (19.08.2016): 239–56. http://dx.doi.org/10.1007/s13194-016-0155-z.
Pełny tekst źródłaCarreira-Perpinan, Miguel Á., i Suryabhan Singh Hada. "Very Fast, Approximate Counterfactual Explanations for Decision Forests". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 6 (26.06.2023): 6935–43. http://dx.doi.org/10.1609/aaai.v37i6.25848.
Pełny tekst źródłaChalyi, Serhii, Volodymyr Leshchynskyi i 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, nr 2 (6) (28.12.2021): 41–46. http://dx.doi.org/10.20998/2079-0023.2021.02.07.
Pełny tekst źródłaAdmassu, Tsehay. "Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection". Proceedings of Engineering and Technology Innovation 23 (1.01.2023): 48–59. http://dx.doi.org/10.46604/peti.2023.10101.
Pełny tekst źródłaZahedi, Zahra, Sailik Sengupta i 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, nr 9 (24.03.2024): 10243–51. http://dx.doi.org/10.1609/aaai.v38i9.28890.
Pełny tekst źródłaNolan, Daniel. "The Possibilities of History". Journal of the Philosophy of History 10, nr 3 (17.11.2016): 441–56. http://dx.doi.org/10.1163/18722636-12341346.
Pełny tekst źródłade Brito Duarte, Regina, Filipa Correia, Patrícia Arriaga i Ana Paiva. "AI Trust: Can Explainable AI Enhance Warranted Trust?" Human Behavior and Emerging Technologies 2023 (31.10.2023): 1–12. http://dx.doi.org/10.1155/2023/4637678.
Pełny tekst źródłaFreiesleben, Timo. "The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples". Minds and Machines 32, nr 1 (30.10.2021): 77–109. http://dx.doi.org/10.1007/s11023-021-09580-9.
Pełny tekst źródłaGuidotti, Riccardo, Anna Monreale, Fosca Giannotti, Dino Pedreschi, Salvatore Ruggieri i Franco Turini. "Factual and Counterfactual Explanations for Black Box Decision Making". IEEE Intelligent Systems 34, nr 6 (1.11.2019): 14–23. http://dx.doi.org/10.1109/mis.2019.2957223.
Pełny tekst źródłaWang, Xiangmeng, Qian Li, Dianer Yu, Qing Li i Guandong Xu. "Counterfactual Explanation for Fairness in Recommendation". ACM Transactions on Information Systems, 29.01.2024. http://dx.doi.org/10.1145/3643670.
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