Relevant bibliographies by topics / Counterfactual Explanation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Counterfactual Explanation'

Author: Grafiati
Published: 7 December 2024

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Journal articles on the topic "Counterfactual Explanation"

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VanNostrand, Peter M., Huayi Zhang, Dennis M. Hofmann, and Elke A. Rundensteiner. "FACET: Robust Counterfactual Explanation Analytics." Proceedings of the ACM on Management of Data 1, no. 4 (2023): 1–27. http://dx.doi.org/10.1145/3626729.

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Machine learning systems are deployed in domains such as hiring and healthcare, where undesired classifications can have serious ramifications for the user. Thus, there is a rising demand for explainable AI systems which provide actionable steps for lay users to obtain their desired outcome. To meet this need, we propose FACET, the first explanation analytics system which supports a user in interactively refining counterfactual explanations for decisions made by tree ensembles. As FACET's foundation, we design a novel type of counterfactual explanation called the counterfactual region. Unlike
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Sia, Suzanna, Anton Belyy, Amjad Almahairi, Madian Khabsa, Luke Zettlemoyer, and Lambert Mathias. "Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9837–45. http://dx.doi.org/10.1609/aaai.v37i8.26174.

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Evaluating an explanation's faithfulness is desired for many reasons such as trust, interpretability and diagnosing the sources of model's errors. In this work, which focuses on the NLI task, we introduce the methodology of Faithfulness-through-Counterfactuals, which first generates a counterfactual hypothesis based on the logical predicates expressed in the explanation, and then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic (i.e. if the new formula is \textit{logically satisfiable}). In contrast to existing approaches, this does not require
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Asher, Nicholas, Lucas De Lara, Soumya Paul, and Chris Russell. "Counterfactual Models for Fair and Adequate Explanations." Machine Learning and Knowledge Extraction 4, no. 2 (2022): 316–49. http://dx.doi.org/10.3390/make4020014.

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Recent efforts have uncovered various methods for providing explanations that can help interpret the behavior of machine learning programs. Exact explanations with a rigorous logical foundation provide valid and complete explanations, but they have an epistemological problem: they are often too complex for humans to understand and too expensive to compute even with automated reasoning methods. Interpretability requires good explanations that humans can grasp and can compute. We take an important step toward specifying what good explanations are by analyzing the epistemically accessible and pra
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Si, Michelle, and Jian Pei. "Counterfactual Explanation of Shapley Value in Data Coalitions." Proceedings of the VLDB Endowment 17, no. 11 (2024): 3332–45. http://dx.doi.org/10.14778/3681954.3682004.

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The Shapley value is widely used for data valuation in data markets. However, explaining the Shapley value of an owner in a data coalition is an unexplored and challenging task. To tackle this, we formulate the problem of finding the counterfactual explanation of Shapley value in data coalitions. Essentially, given two data owners A and B such that A has a higher Shapley value than B , a counter-factual explanation is a smallest subset of data entries in A such that transferring the subset from A to B makes the Shapley value of A less than that of B. We show that counterfactual explanations al
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Baron, Sam. "Counterfactual Scheming." Mind 129, no. 514 (2019): 535–62. http://dx.doi.org/10.1093/mind/fzz008.

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Abstract Mathematics appears to play a genuine explanatory role in science. But how do mathematical explanations work? Recently, a counterfactual approach to mathematical explanation has been suggested. I argue that such a view fails to differentiate the explanatory uses of mathematics within science from the non-explanatory uses. I go on to offer a solution to this problem by combining elements of the counterfactual theory of explanation with elements of a unification theory of explanation. The result is a theory according to which a counterfactual is explanatory when it is an instance of a g
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Baron, Sam, Mark Colyvan, and David Ripley. "A Counterfactual Approach to Explanation in Mathematics." Philosophia Mathematica 28, no. 1 (2019): 1–34. http://dx.doi.org/10.1093/philmat/nkz023.

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ABSTRACT Our goal in this paper is to extend counterfactual accounts of scientific explanation to mathematics. Our focus, in particular, is on intra-mathematical explanations: explanations of one mathematical fact in terms of another. We offer a basic counterfactual theory of intra-mathematical explanations, before modelling the explanatory structure of a test case using counterfactual machinery. We finish by considering the application of counterpossibles to mathematical explanation, and explore a second test case along these lines.
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Chapman-Rounds, Matt, Umang Bhatt, Erik Pazos, Marc-Andre Schulz, and Konstantinos Georgatzis. "FIMAP: Feature Importance by Minimal Adversarial Perturbation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11433–41. http://dx.doi.org/10.1609/aaai.v35i13.17362.

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Instance-based model-agnostic feature importance explanations (LIME, SHAP, L2X) are a popular form of algorithmic transparency. These methods generally return either a weighting or subset of input features as an explanation for the classification of an instance. An alternative literature argues instead that counterfactual instances, which alter the black-box model's classification, provide a more actionable form of explanation. We present Feature Importance by Minimal Adversarial Perturbation (FIMAP), a neural network based approach that unifies feature importance and counterfactual explanatio
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DOHRN, DANIEL. "Counterfactual Narrative Explanation." Journal of Aesthetics and Art Criticism 67, no. 1 (2009): 37–47. http://dx.doi.org/10.1111/j.1540-6245.2008.01333.x.

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Leofante, Francesco, and Nico Potyka. "Promoting Counterfactual Robustness through Diversity." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21322–30. http://dx.doi.org/10.1609/aaai.v38i19.30127.

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Counterfactual explanations shed light on the decisions of black-box models by explaining how an input can be altered to obtain a favourable decision from the model (e.g., when a loan application has been rejected). However, as noted recently, counterfactual explainers may lack robustness in the sense that a minor change in the input can cause a major change in the explanation. This can cause confusion on the user side and open the door for adversarial attacks. In this paper, we study some sources of non-robustness. While there are fundamental reasons for why an explainer that returns a single
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He, Ming, Boyang An, Jiwen Wang, and Hao Wen. "CETD: Counterfactual Explanations by Considering Temporal Dependencies in Sequential Recommendation." Applied Sciences 13, no. 20 (2023): 11176. http://dx.doi.org/10.3390/app132011176.

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Providing interpretable explanations can notably enhance users’ confidence and satisfaction with regard to recommender systems. Counterfactual explanations demonstrate remarkable performance in the realm of explainable sequential recommendation. However, current counterfactual explanation models designed for sequential recommendation overlook the temporal dependencies in a user’s past behavior sequence. Furthermore, counterfactual histories should be as similar to the real history as possible to avoid conflicting with the user’s genuine behavioral preferences. This paper presents counterfactua
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Dissertations / Theses on the topic "Counterfactual Explanation"

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Broadbent, Alex. "A reverse counterfactual analysis of causation." Thesis, University of Cambridge, 2007. https://www.repository.cam.ac.uk/handle/1810/226170.

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Lewis's counterfactual analysis of causation starts with the claim that c causes e if ~ C > ~ E, where c and e are events, C and E are the propositions that c and e respectively occur, ~ is negation and > is the counterfactual conditional. The purpose of my project is to provide a counterfactual analysis of causation which departs signigicantly from Lewis's starting point, and thus can hope to solve several stubborn problems for that approach. Whereas Lewis starts with a sufficiency claim, my analysis claims that a certain counterfactual is necessary for causation. I say that, if c causes e, t
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Jeanneret, Sanmiguel Guillaume. "Towards explainable and interpretable deep neural networks." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC229.

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Les architectures neuronales profondes ont démontré des résultats remarquables dans diverses tâches de vision par ordinateur. Cependant, leur performance extraordinaire se fait au détriment de l'interprétabilité. En conséquence, le domaine de l'IA explicable a émergé pour comprendre réellement ce que ces modèles apprennent et pour découvrir leurs sources d'erreur. Cette thèse explore les algorithmes explicables afin de révéler les biais et les variables utilisés par ces modèles de boîte noire dans le contexte de la classification d'images. Par conséquent, nous divisons cette thèse en quatre pa
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Jeyasothy, Adulam. "Génération d'explications post-hoc personnalisées." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS027.

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La thèse se place dans le domaine de l'IA explicable (XAI, eXplainable AI). Nous nous concentrons sur les méthodes d'interprétabilité post-hoc qui visent à expliquer à un utilisateur la prédiction pour une donnée d'intérêt spécifique effectuée par un modèle de décision entraîné. Pour augmenter l'interprétabilité des explications, cette thèse étudie l'intégration de connaissances utilisateur dans ces méthodes, et vise ainsi à améliorer la compréhensibilité de l'explication en générant des explications personnalisées adaptées à chaque utilisateur. Pour cela, nous proposons un formalisme général
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Lerouge, Mathieu. "Designing and generating user-centered explanations about solutions of a Workforce Scheduling and Routing Problem." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPAST174.

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Les systèmes d'aide à la décision basés sur l'optimisation combinatoire trouvent des applications dans divers domaines professionnels. Cependant, les décideurs qui utilisent ces systèmes ne comprennent souvent pas les concepts mathématiques et les principes algorithmiques qui les sous-tendent. Ce manque de compréhension peut entraîner du scepticisme et une réticence à accepter les solutions générées par le système, érodant ainsi la confiance placée dans le système. Cette thèse traite cette problématique dans le cas du problème de planification d'employés mobiles, en anglais Workforce Schedulin
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Kuo, Chia-Yu, and 郭家諭. "Explainable Risk Prediction System for Child Abuse Event by Individual Feature Attribution and Counterfactual Explanation." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/yp2nr3.

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碩士<br>國立交通大學<br>統計學研究所<br>107<br>There always have a trade-off: Performance or Interpretability. The complex model, such as ensemble learning can achieve outstanding prediction accuracy. However, it is not easy to interpret the complex model. Understanding why a model made a prediction help us to trust the black-box model, and also help users to make decisions. This work plans to use the techniques of explainable machine learning to develop the appropriate model for empirical data with high prediction and good interpretability. In this study, we use the data provided by Taipei City Center for
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Books on the topic "Counterfactual Explanation"

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Reutlinger, Alexander. Extending the Counterfactual Theory of Explanation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198777946.003.0005.

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In the recent debate on explanation philosophers tend to agree that the natural and social sciences do not only provide causal but also non-causal explanations. It is a challenging aspect of this agreement that currently dominant causal accounts of explanation fail to cover non-causal types of explanation. So, how shall we react to this challenge? The goal of this chapter is to articulate and to extend the counterfactual theory of explanation (CTE). The CTE is a monist account of explanation. Monism is the view that there is one single philosophical account capturing both causal and non-causal
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Brady, Henry E. Causation and Explanation in Social Science. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0010.

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This article provides an overview of causal thinking by characterizing four approaches to causal inference. It also describes the INUS model. It specifically presents a user-friendly synopsis of philosophical and statistical musings about causation. The four approaches to causality include neo-Humean regularity, counterfactual, manipulation and mechanisms, and capacities. A counterfactual is a statement, typically in the subjunctive mood, in which a false or ‘counter to fact’ premise is followed by some assertion about what would have happened if the premise were true. Three basic questions ab
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Gerstenberg, Tobias, and Joshua B. Tenenbaum. Intuitive Theories. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.28.

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This chapter first explains what intuitive theories are, how they can be modeled as probabilistic, generative programs, and how intuitive theories support various cognitive functions such as prediction, counterfactual reasoning, and explanation. It focuses on two domains of knowledge: people’s intuitive understanding of physics, and their intuitive understanding of psychology. It shows how causal judgments can be modeled as counterfactual contrasts operating over an intuitive theory of physics, and how explanations of an agent’s behavior are grounded in a rational planning model that is invert
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McGregor, Rafe. A Criminology Of Narrative Fiction. Policy Press, 2021. http://dx.doi.org/10.1332/policypress/9781529208054.001.0001.

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This book answers the question of the usefulness of criminological fiction. Criminological fiction is fiction that can provide an explanation of the causes of crime or social harm and could, in consequence, contribute to the development of crime or social harm reduction policies. The book argues that criminological fiction can provide at least the following three types of criminological knowledge: (1) phenomenological, i.e. representing what certain experiences are like; (2) counterfactual, i.e. representing possible but non-existent situations; and (3) mimetic, i.e. representing everyday real
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Kutach, Douglas. The Asymmetry of Influence. Edited by Craig Callender. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199298204.003.0009.

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This chapter considers the nature of the causal asymmetry, or even more generally, the asymmetry of influence. Putting aside explanations which would appeal to an asymmetry in time as explaining this asymmetry, it aims to show, using current physical theory and no ad hoc time asymmetric assumptions, why it is that future-directed influence sometimes advances one's goals but backward-directed influence does not. The chapter claims that agency is crucial to the explanation of the influence asymmetry. It provides an exhaustive account of the advancement asymmetry that is connected with fundamenta
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Silberstein, Michael, W. M. Stuckey, and Timothy McDevitt. Relational Blockworld and Quantum Mechanics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198807087.003.0005.

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The main thread of chapter 4 introduces some of the major mysteries and interpretational issues of quantum mechanics (QM). These mysteries and issues include: quantum superposition, quantum nonlocality, Bell’s inequality, entanglement, delayed choice, the measurement problem, and the lack of counterfactual definiteness. All these mysteries and interpretational issues of QM result from dynamical explanation in the mechanical universe and are dispatched using the authors’ adynamical explanation in the block universe, called Relational Blockworld (RBW). A possible link between RBW and quantum inf
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St John, Taylor. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789918.003.0001.

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This chapter sets out the puzzle: what explains the rise of investor–state arbitration? It defines the rise of investor–state arbitration as one process with two phases: the creation of the ICSID Convention and eliciting state consent to ISDS. Conventional theoretical accounts, in which investor lobbying and then intergovernmental bargaining drive the rise of investor–state arbitration, are outlined. These accounts contrast with the book’s explanation, that international officials provided support to one institutional framework, ICSID, which led to its creation over other possibilities. The cr
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Stock, Kathleen. Fiction, Belief, and ‘Imaginative Resistance’. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198798347.003.0005.

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The chapter starts with a focus on the relation between fiction and the inculcation of justified belief via testimony. The claim, relied upon in Chapter 3, that fictions can be sources of testimony and so justified belief, is defended. Then the fact that fictive utterances can, effectively, instruct readers to have beliefs, is implicated in a new explanation of ‘imaginative resistance’. The author suggests that the right account of this phenomenon should cite the reader’s perception of an authorial intention that she believe a counterfactual, which in fact she cannot believe. This view is defe
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Healey, Richard. Causation and Locality. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198714057.003.0010.

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By moving to the context of relativistic space-time structure, this chapter completes the argument of Chapter 4 that we can use quantum theory locally to explain correlations that violate Bell inequalities with no instantaneous action at a distance. Chance here must be relativized not just to time but to a space-time point, so that an event may have more than one chance at the same time—it may even be certain relative to one space-time point but ‘at the same time’ completely uncertain relative to another. This renders Bell’s principle of Local Causality either inapplicable or intuitively unmot
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Berezin, Mabel. Events as Templates of Possibility: An Analytic Typology of Political Facts. Edited by Jeffrey C. Alexander, Ronald N. Jacobs, and Philip Smith. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780195377767.013.23.

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This article extends the concept of events to bring cultural analysis to bear on political explanation and privileges “thick description” and narrative as methodological tools. Drawing on the views of Emile Durkheim, it argues that events constitute “social facts”—phenomena with sufficient identity and coherence that the social collectivity recognizes them as discrete and important. The article first considers the tension between the political and the cultural using a metaphor from sports and biology that unites agency and nature. It then discusses the intersection of events and experience as
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Book chapters on the topic "Counterfactual Explanation"

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Virmajoki, Veli. "A Counterfactual Account of Historiographical Explanation." In Causal Explanation in Historiography. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45929-0_5.

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Willig, Moritz, Matej Zečević, and Kristian Kersting. "“Do Not Disturb My Circles!” Identifying the Type of Counterfactual at Hand (Short Paper)." In Robust Argumentation Machines. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63536-6_16.

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AbstractWhen the phenomena of interest are in need of explanation, we are often in search of the underlying root causes. Causal inference provides tools for identifying these root causes—by performing interventions on suitably chosen variables we can observe down-stream effects in the outcome variable of interest. On the other hand, argumentation as an approach of attributing observed outcomes to specific factors, naturally lends itself as a tool for determining the most plausible explanation. We can further improve the robustness of such explanations by measuring their likelihood within a mutually agreed-upon causal model. For this, typically one of in-principle two distinct types of counterfactual explanations is used: interventional counterfactuals, which treat changes as deliberate interventions to the causal system, and backtracking counterfactuals, which attribute changes exclusively to exogenous factors. Although both frameworks share the common goal of inferring true causal factors, they fundamentally differ in their conception of counterfactuals. Here, we present the first approach that decides when to expect interventional and when to opt for backtracking counterfactuals.
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Gerber, Doris. "Counterfactual Causality and Historical Explanations." In Explanation in Action Theory and Historiography. Routledge, 2019. http://dx.doi.org/10.4324/9780429506048-9.

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Cheng, He, Depeng Xu, Shuhan Yuan, and Xintao Wu. "Achieving Counterfactual Explanation for Sequence Anomaly Detection." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70371-3_2.

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Singh, Vandita, Kristijonas Cyras, and Rafia Inam. "Explainability Metrics and Properties for Counterfactual Explanation Methods." In Explainable and Transparent AI and Multi-Agent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15565-9_10.

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Stepin, Ilia, Alejandro Catala, Martin Pereira-Fariña, and Jose M. Alonso. "Factual and Counterfactual Explanation of Fuzzy Information Granules." In Studies in Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64949-4_6.

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Li, Peiyu, Omar Bahri, Soukaïna Filali Boubrahimi, and Shah Muhammad Hamdi. "Attention-Based Counterfactual Explanation for Multivariate Time Series." In Big Data Analytics and Knowledge Discovery. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39831-5_26.

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Ji, Jiemin, Donghai Guan, Weiwei Yuan, and Yuwen Deng. "Unified Counterfactual Explanation Framework for Black-Box Models." In PRICAI 2023: Trends in Artificial Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7025-4_36.

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Burkart, Nadia, Maximilian Franz, and Marco F. Huber. "Explanation Framework for Intrusion Detection." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_9.

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AbstractMachine learning and deep learning are widely used in various applications to assist or even replace human reasoning. For instance, a machine learning based intrusion detection system (IDS) monitors a network for malicious activity or specific policy violations. We propose that IDSs should attach a sufficiently understandable report to each alert to allow the operator to review them more efficiently. This work aims at complementing an IDS by means of a framework to create explanations. The explanations support the human operator in understanding alerts and reveal potential false positives. The focus lies on counterfactual instances and explanations based on locally faithful decision-boundaries.
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Peng, Bo, Siwei Lyu, Wei Wang, and Jing Dong. "Counterfactual Image Enhancement for Explanation of Face Swap Deepfakes." In Pattern Recognition and Computer Vision. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18910-4_40.

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Conference papers on the topic "Counterfactual Explanation"

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Liu, Diwen, and Xiaodong Yue. "Counterfactual-Driven Model Explanation Evaluation Method." In 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2024. http://dx.doi.org/10.1109/cisce62493.2024.10653060.

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Fan, Zhengyang, Wanru Li, Kathryn Blackmond Laskey, and Kuo-Chu Chang. "Towards Personalized Anti-Phishing: Counterfactual Explanation Approach - Extended Abstract." In 2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2024. http://dx.doi.org/10.1109/dsaa61799.2024.10722801.

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Yin, Xiang, Nico Potyka, and Francesca Toni. "CE-QArg: Counterfactual Explanations for Quantitative Bipolar Argumentation Frameworks." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/66.

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There is a growing interest in understanding arguments' strength in Quantitative Bipolar Argumentation Frameworks (QBAFs). Most existing studies focus on attribution-based methods that explain an argument's strength by assigning importance scores to other arguments but fail to explain how to change the current strength to a desired one. To solve this issue, we introduce counterfactual explanations for QBAFs. We discuss problem variants and propose an iterative algorithm named Counterfactual Explanations for Quantitative bipolar Argumentation frameworks (CE-QArg). CE-QArg can identify valid and
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Alfano, Gianvincenzo, Sergio Greco, Francesco Parisi, and Irina Trubitsyna. "Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/2.

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Explainable Artificial Intelligence and Formal Argumentation have received significant attention in recent years. Argumentation frameworks are useful for representing knowledge and reasoning on it. Counterfactual and semifactual explanations are interpretability techniques that provide insights into the outcome of a model by generating alternative hypothetical instances. While there has been important work on counterfactual and semifactual explanations for Machine Learning (ML) models, less attention has been devoted to these kinds of problems in argumentation. In this paper, we explore counte
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Theobald, Claire, Frédéric Pennerath, Brieuc Conan-Guez, Miguel Couceiro, and Amedeo Napoli. "Clarity: a Deep Ensemble for Visual Counterfactual Explanations." In ESANN 2024. Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-188.

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Molhoek, M., and J. Van Laanen. "Secure Counterfactual Explanations in a Two-party Setting." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706413.

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Leofante, Francesco, Elena Botoeva, and Vineet Rajani. "Counterfactual Explanations and Model Multiplicity: a Relational Verification View." In 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/78.

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We study the interplay between counterfactual explanations and model multiplicity in the context of neural network classifiers. We show that current explanation methods often produce counterfactuals whose validity is not preserved under model multiplicity. We then study the problem of generating counterfactuals that are guaranteed to be robust to model multiplicity, characterise its complexity and propose an approach to solve this problem using ideas from relational verification.
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Yin, Xudong, and Yao Yang. "CMACE: CMAES-based Counterfactual Explanations for Black-box Models." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/60.

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Explanatory Artificial Intelligence plays a vital role in machine learning, due to its widespread application in decision-making scenarios, e.g., credit lending. Counterfactual Explanation (CFE) is a new kind of explanatory method that involves asking “what if ”, i.e. what would have happened if model inputs slightly change. To answer the question, Counterfactual Explanation aims at finding a minimum perturbation in model inputs leading to a different model decision. Compared with model-agnostic approaches, model-specific CFE approaches designed only for specific type of models usually have be
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Bhan, Milan, Jean-noel Vittaut, Nicolas Chesneau, and Marie-jeanne Lesot. "Enhancing textual counterfactual explanation intelligibility through Counterfactual Feature Importance." In Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023). Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.trustnlp-1.19.

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Aryal, Saugat, and Mark T. Keane. "Even If Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/732.

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Recently, eXplainable AI (XAI) research has focused on counterfactual explanations as post-hoc justifications for AI-system decisions (e.g., a customer refused a loan might be told “if you asked for a loan with a shorter term, it would have been approved”). Counterfactuals explain what changes to the input-features of an AI system change the output-decision. However, there is a sub-type of counterfactual, semi-factuals, that have received less attention in AI (though the Cognitive Sciences have studied them more). This paper surveys semi-factual explanation, summarising historical and recent w
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Reports on the topic "Counterfactual Explanation"

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Gandelman, Néstor. A Comparison of Saving Rates: Micro Evidence from Seventeen Latin American and Caribbean Countries. Inter-American Development Bank, 2015. http://dx.doi.org/10.18235/0011701.

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Using micro data on expenditure and income for 17 Latin American and Caribbean (LAC) countries, this paper presents stylized facts on saving behavior by age, education, income and place of residence. Counterfactual saving rates are computed by imposing the saving behavior, the population distribution or the income distribution of two benchmark economies (the United States and Korea). The results suggest that the difference in national saving rates between LAC and the benchmark economies can mainly be attributed to differences in saving behavior of the population and, to a lesser extent, to dif
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