Literatura científica selecionada sobre o tema "Scenario and counterfactual explanations"
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Artigos de revistas sobre o assunto "Scenario and counterfactual explanations"
Labaien Soto, Jokin, Ekhi Zugasti Uriguen e Xabier De Carlos Garcia. "Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders". Applied Sciences 13, n.º 5 (24 de fevereiro de 2023): 2912. http://dx.doi.org/10.3390/app13052912.
Texto completo da fonteVirmajoki, Veli. "Frameworks in Historiography: Explanation, Scenarios, and Futures". Journal of the Philosophy of History 17, n.º 2 (3 de julho de 2023): 288–309. http://dx.doi.org/10.1163/18722636-12341501.
Texto completo da fonteCrawford, Beverly. "Germany's Future Political Challenges: Imagine that The New Yorker Profiled the German Chancellor in 2015". German Politics and Society 23, n.º 4 (1 de dezembro de 2005): 69–87. http://dx.doi.org/10.3167/gps.2005.230404.
Texto completo da fonteNolan, Daniel. "The Possibilities of History". Journal of the Philosophy of History 10, n.º 3 (17 de novembro de 2016): 441–56. http://dx.doi.org/10.1163/18722636-12341346.
Texto completo da fonteRahimi, Saeed, Antoni B. Moore e Peter A. Whigham. "Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements". ISPRS International Journal of Geo-Information 10, n.º 3 (22 de março de 2021): 190. http://dx.doi.org/10.3390/ijgi10030190.
Texto completo da fonteDelaney, Eoin, Arjun Pakrashi, Derek Greene e 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 março de 2024): 22696. http://dx.doi.org/10.1609/aaai.v38i20.30596.
Texto completo da fonteBarzekar, Hosein, e 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 completo da fonteBaron, Sam, Mark Colyvan e David Ripley. "A Counterfactual Approach to Explanation in Mathematics". Philosophia Mathematica 28, n.º 1 (2 de dezembro de 2019): 1–34. http://dx.doi.org/10.1093/philmat/nkz023.
Texto completo da fonteFernández-Loría, Carlos, Foster Provost e Xintian Han. "Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach". MIS Quarterly 45, n.º 3 (1 de setembro de 2022): 1635–60. http://dx.doi.org/10.25300/misq/2022/16749.
Texto completo da fontePrado-Romero, Mario Alfonso, Bardh Prenkaj e Giovanni Stilo. "Robust Stochastic Graph Generator for Counterfactual Explanations". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 19 (24 de março de 2024): 21518–26. http://dx.doi.org/10.1609/aaai.v38i19.30149.
Texto completo da fonteTeses / dissertações sobre o assunto "Scenario and counterfactual explanations"
Jeyasothy, Adulam. "Génération d'explications post-hoc personnalisées". Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS027.
Texto completo da fonteThis thesis is in the field of eXplainable AI (XAI). We focus on post-hoc interpretability methods that aim to explain to a user the prediction for a specific data made by a trained decision model. To increase the interpretability of explanations, this thesis studies the integration of user knowledge into these methods, and thus aims to improve the understandability of the explanation by generating personalized explanations tailored to each user. To this end, we propose a general formalism that explicitly integrates knowledge via a new criterion in the interpretability objectives. This formalism is then declined for different types of knowledge and different types of explanations, particularly counterfactual examples, leading to the proposal of several algorithms (KICE, Knowledge Integration in Counterfactual Explanation, rKICE for its variant including knowledge expressed by rules and KISM, Knowledge Integration in Surrogate Models). The issue of aggregating classical quality and knowledge compatibility constraints is also studied, and we propose to use Gödel's integral as an aggregation operator. Finally, we discuss the difficulty of generating a single explanation suitable for all types of users and the notion of diversity in explanations
Ball, Russell Andrew. "An investigation of biased depictions of normality in counterfactual scenario studies". 2004. http://hdl.handle.net/1828/546.
Texto completo da fonteCharrua, Vera Veríssimo Agostinho Sabino. "Pensamento contrafactual : ator e leitor em cenários negativos e positivos". Master's thesis, 2021. http://hdl.handle.net/10400.12/8098.
Texto completo da fonteO Pensamento Contrafactual é um tipo de pensamento espontâneo que nos permite procurar alternativas à nossa realidade, muitas vezes evidenciado através da expressão “e se…”, que ocorre frequentemente no dia-a-dia de qualquer sujeito (Byrne, 2016). Já em muitos estudos foi sugerido o impacto que certas variáveis como a posição do sujeito (ator vs. leitor) (Girotto e. al., 2007; Pighin et. al., 2011), ou o cenário/valência do resultado (sucesso/positivo vs. insucesso/negativo) (Roese & Olson, 1993a, 1993b) têm em diversas dimensões do PCF. Neste estudo acrescenta-se uma pergunta sobre este assunto: será que os sujeitos na posição de atores ou leitores têm pensamentos contrafactuais, perante um resultado positivo, idênticos aos que já se conhece perante um resultado negativo (Girotto e. al., 2007; Pighin et. al., 2011)? Assim, hipotetizou-se que, por um lado, deixaria de haver diferenças entre atores e leitores em cenário de sucesso, e, por outro, que o tipo de cenário teria apenas impacto em atores e não em leitores. Os resultados foram no sentido de ambas as hipóteses. Adicionalmente, foram propostas outras duas hipóteses: uma diz respeito à relação entre a valência de resultado e à estrutura do PCF (quanto aos atores, seria esperado que em cenário positivo fossem elaborados contrafactuais de estrutura subtrativa e em cenário negativo fossem elaborados contrafactuais de estrutura aditiva, não sendo esperadas diferenças entre os leitores); e outra diz respeito à relação entre a valência de resultado e o tipo de atribuição causal (interna vs, externa) (era esperado que, em grupos de atores, se atribuísse o sucesso a fatores internos e o insucesso a fatores externos, não se esperando diferenças entre os leitores). Os resultados relativos a estas últimas hipóteses não foram no sentido do esperado.
Counterfactual thought is a type of thought which allows us to search for mental alternatives to our present reality. It’s spontaneous and frequent in our everyday lives, and it is frequently expressed through a “what if…” conjunction (Byrne, 2016). There have been many studies in which it was suggested that variables such as role (actor vs reader) (Girotto e. al., 2007; Pighin et. al., 2011) or scenario/outcome valence (positive vs. negative) (Roese & Olson, 1993a, 1993b) have impact on certain dimensions of counterfactual though. In this study, we add up yet another question about this issue: do both actors and readers have the same counterfactual thoughts for both negative and positive scenarios? Thereby, we hypothesised that there would be no differences between roles on a positive scenario, and that the difference in scenario would only impact on actors but not on readers. The results went accordingly to the proposed hypotheses. Additionally, we proposed yet another two hypotheses regarding outcome valence and the structure of counterfactual thought (additive vs. subtractive) (within the actor groups, it was expected that on a positive scenario, subjects would elaborate more subtractive counterfactual thoughts and on a negative scenario, subjects would elaborate more additive counterfactual thoughts, not expecting any differences between the two groups of readers), and outcome valence and causal attribution (it was expected, within the actor groups, that success were attributed to internal elements and unsuccess attributed to external elements; within readers, there were no differences to be expected, no matter the outcome valence). The results concerning these last hypotheses didn’t go accordingly to that which we proposed.
(9375209), Cristhian Lizarazo Jimenez. "IDENTIFICATION OF FAILURE-CAUSED TRAFFIC CONFLICTS IN TRACKING SYSTEMS: A GENERAL FRAMEWORK". Thesis, 2020.
Encontre o texto completo da fonteTraffic conflicts are one of the most widely adopted surrogate measures of safety because they meet the following two conditions for crash surrogacy: (1) they are non-crash events that can be physically related in a predictable and reliable way to crashes, and (2) there is a potential for bridging crash frequency and severity with traffic conflicts. However, three primary issues were identified in the literature that need to be resolved for the practical application of conflicts: (1) the lack of consistency in the definition of traffic conflict, (2) the predictive validity from such events, and (3) the adequacy of traffic conflict observations.
Tarko (2018) developed a theoretical framework in response to the first two issues and defined traffic conflicts using counterfactual theory as events where the lack of timely responses from drivers or road users can produce crashes if there is no evasive action. The author further introduced a failure-based definition to emphasize conflicts as an undesirable condition that needs to be corrected to avoid a crash. In this case, the probability of a crash, given failure, depends on the response delay. The distribution of this delay is adjusted, and the probability is estimated using the fitted distribution. As this formal theory addresses the first two issues, a complete framework for the proper identification of conflicts needs to be investigated in line with the failure mechanism proposed in this theory.
The objective of this dissertation, in response to the third issue, is to provide a generalized framework for proper identification of traffic conflicts by considering the failure-based definition of traffic conflicts. The framework introduced in this dissertation is built upon an empirical evaluation of the methods applied to identify traffic conflicts from naturalistic driving studies and video-based tracking systems. This dissertation aimed to prove the practicality of the framework for proactive safety evaluation using emerging technologies from in-vehicle and roadside instrumentation.
Two conditions must be met to properly claim observed traffic events as traffic conflicts: (1) analysis of longitudinal and lateral acceleration profiles for identification of response due to failure and (2) estimation of the time-to-collision as the period between the end of the evasion and the hypothetical collision. Extrapolating user behavior in the counterfactual scenario of no evasion is applied for identifying the hypothetical collision point.
The results from the SHRP2 study were particularly encouraging, where the appropriate identification of traffic conflicts resulted in the estimation of an expected number of crashes similar to the number reported in the study. The results also met the theoretical postulates including stabilization of the estimated crashes at lower proximity values and Lomax-distributed response delays. In terms of area-wide tracking systems, the framework was successful in identifying and removing failure-free encounters from the In-Depth understanding of accident causation for Vulnerable road users (InDeV) program.
This dissertation also extended the application of traffic conflicts technique by considering estimation of the severity of a hypothetical crash given that a conflict occurs. This component is important in order for conflicts to resemble the practical applications of crashes, including the diagnostics of hazardous locations and evaluating the effectiveness of the countermeasures. Countermeasures should not only reduce the number of conflicts but also the risk of crash given the conflict. Severity analysis identifies the environmental, road, driver, and pre-crash conditions that increase the likelihood of severe impacts. Using dynamic characterization of crash events, this dissertation structured a probability model to evaluate crash reporting and its associated severity. Multinomial logistic models were applied in the estimation; and quasi-complete separation in logistic regression was addressed by providing a Bayesian estimation of these models.
Livros sobre o assunto "Scenario and counterfactual explanations"
Reutlinger, Alexander. Extending the Counterfactual Theory of Explanation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198777946.003.0005.
Texto completo da fontePRO, Certified. Certified Facility Manager Practice Exam: 100 Scenario Based Questions and Answers with Explanations. Independently Published, 2018.
Encontre o texto completo da fonteAnup Kumar S. Pmp PgMP. Program Management Professional Question Bank: 440 Advance Scenario Questions and Answers with Explanations. Independently Published, 2018.
Encontre o texto completo da fonteGerstenberg, Tobias, e Joshua B. Tenenbaum. Intuitive Theories. Editado por Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.28.
Texto completo da fonteRubab, Fariha. Microsoft 365 Fundamentals Certification MS-900: Exam Practice Questions Updated 2022 - Accelerate Your Exam Success with Real Questions with Answers and Explanations Scenario Based. Independently Published, 2022.
Encontre o texto completo da fonteFrench, Steven, e Juha Saatsi. Symmetries and Explanatory Dependencies in Physics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198777946.003.0010.
Texto completo da fonteKutach, Douglas. The Asymmetry of Influence. Editado por Craig Callender. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199298204.003.0009.
Texto completo da fonteHassini, Achref. PMP EXAM PREP QUESTIONS 2021 - 2022 Exam Simulator: 180 Situational, and Scenario-Based Questions l Close to the Real PMP Exam l + Detailed Answers Explanations l Covering the Current PMP Exam. Independently Published, 2021.
Encontre o texto completo da fonteSolstad, Torgrim, e Oliver Bott. Causality and Causal Reasoning in Natural Language. Editado por Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.32.
Texto completo da fonteMaron, Martine. Is “no net loss of biodiversity” a good idea? Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198808978.003.0022.
Texto completo da fonteCapítulos de livros sobre o assunto "Scenario and counterfactual explanations"
Kuhl, Ulrike, André Artelt e Barbara Hammer. "For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI". In Communications in Computer and Information Science, 280–300. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44070-0_14.
Texto completo da fonteHolzinger, Andreas, Anna Saranti, Anne-Christin Hauschild, Jacqueline Beinecke, Dominik Heider, Richard Roettger, Heimo Mueller, Jan Baumbach e Bastian Pfeifer. "Human-in-the-Loop Integration with Domain-Knowledge Graphs for Explainable Federated Deep Learning". In Lecture Notes in Computer Science, 45–64. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40837-3_4.
Texto completo da fonteDandl, Susanne, Christoph Molnar, Martin Binder e Bernd Bischl. "Multi-Objective Counterfactual Explanations". In Parallel Problem Solving from Nature – PPSN XVI, 448–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58112-1_31.
Texto completo da fonteKuratomi, Alejandro, Ioanna Miliou, Zed Lee, Tony Lindgren e Panagiotis Papapetrou. "JUICE: JUstIfied Counterfactual Explanations". In Discovery Science, 493–508. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18840-4_35.
Texto completo da fonteGuyomard, Victor, Françoise Fessant, Thomas Guyet, Tassadit Bouadi e Alexandre Termier. "Generating Robust Counterfactual Explanations". In Machine Learning and Knowledge Discovery in Databases: Research Track, 394–409. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43418-1_24.
Texto completo da fonteGerber, Doris. "Counterfactual Causality and Historical Explanations". In Explanation in Action Theory and Historiography, 167–78. 1 [edition]. | New York : Taylor & Francis, 2019. | Series: Routledge studies in contemporary philosophy ; 121: Routledge, 2019. http://dx.doi.org/10.4324/9780429506048-9.
Texto completo da fonteMishra, Pradeepta. "Counterfactual Explanations for XAI Models". In Practical Explainable AI Using Python, 265–78. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7158-2_10.
Texto completo da fonteJeanneret, Guillaume, Loïc Simon e Frédéric Jurie. "Diffusion Models for Counterfactual Explanations". In Computer Vision – ACCV 2022, 219–37. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26293-7_14.
Texto completo da fonteBoumazouza, Ryma, Fahima Cheikh-Alili, Bertrand Mazure e Karim Tabia. "A Symbolic Approach for Counterfactual Explanations". In Lecture Notes in Computer Science, 270–77. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58449-8_21.
Texto completo da fonteJacob, Paul, Éloi Zablocki, Hédi Ben-Younes, Mickaël Chen, Patrick Pérez e Matthieu Cord. "STEEX: Steering Counterfactual Explanations with Semantics". In Lecture Notes in Computer Science, 387–403. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19775-8_23.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Scenario and counterfactual explanations"
Sokol, Kacper, e Peter Flach. "Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/865.
Texto completo da fonteAlbini, Emanuele, Jason Long, Danial Dervovic e Daniele Magazzeni. "Counterfactual Shapley Additive Explanations". In FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3531146.3533168.
Texto completo da fonteJeanneret, Guillaume, Loïc Simon e Frédéric Jurie. "Adversarial Counterfactual Visual Explanations". In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01576.
Texto completo da fonteRodriguez, Pau, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam Laradji, Laurent Charlin e David Vazquez. "Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations". In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00109.
Texto completo da fonteTran, Khanh Hiep, Azin Ghazimatin e Rishiraj Saha Roy. "Counterfactual Explanations for Neural Recommenders". In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3404835.3463005.
Texto completo da fonteZemni, Mehdi, Mickaël Chen, Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez e Matthieu Cord. "OCTET: Object-aware Counterfactual Explanations". In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01446.
Texto completo da fonteZhao, Wenqi, Satoshi Oyama e Masahito Kurihara. "Generating Natural Counterfactual Visual Explanations". In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/742.
Texto completo da fonteArtelt, Andre, Valerie Vaquet, Riza Velioglu, Fabian Hinder, Johannes Brinkrolf, Malte Schilling e Barbara Hammer. "Evaluating Robustness of Counterfactual Explanations". In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021. http://dx.doi.org/10.1109/ssci50451.2021.9660058.
Texto completo da fonteHan, Chan Sik, e Keon Myung Lee. "Gradient-based Counterfactual Generation for Sparse and Diverse Counterfactual Explanations". In SAC '23: 38th ACM/SIGAPP Symposium on Applied Computing. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3555776.3577737.
Texto completo da fonteWang, Pei, e Nuno Vasconcelos. "SCOUT: Self-Aware Discriminant Counterfactual Explanations". In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00900.
Texto completo da fonteRelatórios de organizações sobre o assunto "Scenario and counterfactual explanations"
Gandelman, Néstor. A Comparison of Saving Rates: Micro Evidence from Seventeen Latin American and Caribbean Countries. Inter-American Development Bank, julho de 2015. http://dx.doi.org/10.18235/0011701.
Texto completo da fonteGeloso, Vincent, e Chandler S. Reilly. Did the ‘Quiet Revolution’ Really Change Anything? CIRANO, dezembro de 2022. http://dx.doi.org/10.54932/itzr4537.
Texto completo da fonteJuárez, Leticia. Buyer Market Power and Exchange Rate Pass-through. Inter-American Development Bank, agosto de 2023. http://dx.doi.org/10.18235/0005083.
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