Relevant bibliographies by topics / Explanation

Contents

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

Academic literature on the topic 'Explanation'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Explanation"

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Atanasova, Pepa, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. "Diagnostics-Guided Explanation Generation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10445–53. http://dx.doi.org/10.1609/aaai.v36i10.21287.

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Explanations shed light on a machine learning model's rationales and can aid in identifying deficiencies in its reasoning process. Explanation generation models are typically trained in a supervised way given human explanations. When such annotations are not available, explanations are often selected as those portions of the input that maximise a downstream task's performance, which corresponds to optimising an explanation's Faithfulness to a given model. Faithfulness is one of several so-called diagnostic properties, which prior work has identified as useful for gauging the quality of an explanation without requiring annotations. Other diagnostic properties are Data Consistency, which measures how similar explanations are for similar input instances, and Confidence Indication, which shows whether the explanation reflects the confidence of the model. In this work, we show how to directly optimise for these diagnostic properties when training a model to generate sentence-level explanations, which markedly improves explanation quality, agreement with human rationales, and downstream task performance on three complex reasoning tasks.
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Clark, Stephen R. L. "The Limits of Explanation: Limited Explanations." Royal Institute of Philosophy Supplement 27 (March 1990): 195–210. http://dx.doi.org/10.1017/s1358246100005117.

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When I was first approached to read a paper at the conference from which this volume takes its beginning I expected that Flint Schier, with whom I had taught a course on the Philosophy of Biology in my years at Glasgow, would be with us to comment and to criticize. I cannot let this occasion pass without expressing once again my own sense of loss. I am sure that we would all have gained by his presence, and hope that he would find things both to approve, and disapprove, in the following venture.
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Kleih, Björn-Christian. "Die mündliche Erklärung zur Abstimmung gemäß § 31 Absatz 1 GOBT – eine parlamentarische Wundertüte mit Potenzial?" Zeitschrift für Parlamentsfragen 51, no. 4 (2020): 865–87. http://dx.doi.org/10.5771/0340-1758-2020-4-865.

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According to the Rules of Procedure of the German Bundestag (”GOBT”), every Member of Parliament is granted a five minutes’ verbal explanation of vote . It is granted for nearly every kind of vote in the House . The verbal explanation is often considered a privilege to MPs going against the position taken by their group . Yet, it is also used to confirm the party position and it is abused to continue already closed debates . In either case, they can be a grab bag for both parliament’s plenum and its president; the verbal explanation’s content is only revealed when the explanation is given . A quantitative and qualitative analysis of the explanations given in the Bundestag shows that explanations from dissenters contribute quantitatively, but not to a large extent . While members of the coalition more often declare to go against their parliamentary party group, members of the opposition tend to confirm the line of their respective party . When used to reveal personal implications in the decision‑making process, the verbal explanation is meaningful and widely accepted .
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Fogelin, Lars. "Inference to the Best Explanation: A Common and Effective Form of Archaeological Reasoning." American Antiquity 72, no. 4 (October 2007): 603–26. http://dx.doi.org/10.2307/25470436.

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Processual and postprocessual archaeologists implicitly employ the same epistemological system to evaluate the worth of different explanations: inference to the best explanation. This is good since inference to the best explanation is the most effective epistemological approach to archaeological reasoning available. Underlying the logic of inference to the best explanation is the assumption that the explanation that accounts for the most evidence is also most likely to be true. This view of explanation often reflects the practice of archaeological reasoning better than either the hypothetico-deductive method or hermeneutics. This article explores the logic of inference to the best explanation and provides clear criteria to determine what makes one explanation better than another. Explanations that are empirically broad, general, modest, conservative, simple, testable, and address many perspectives are better than explanations that are not. This article also introduces a system of understanding explanation that emphasizes the role of contrastive pairings in the construction of specific explanations. This view of explanation allows for a better understanding of when, and when not, to engage in the testing of specific explanations.
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Brdnik, Saša, Vili Podgorelec, and Boštjan Šumak. "Assessing Perceived Trust and Satisfaction with Multiple Explanation Techniques in XAI-Enhanced Learning Analytics." Electronics 12, no. 12 (June 8, 2023): 2594. http://dx.doi.org/10.3390/electronics12122594.

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This study aimed to observe the impact of eight explainable AI (XAI) explanation techniques on user trust and satisfaction in the context of XAI-enhanced learning analytics while comparing two groups of STEM college students based on their Bologna study level, using various established feature relevance techniques, certainty, and comparison explanations. Overall, the students reported the highest trust in local feature explanation in the form of a bar graph. Additionally, master’s students presented with global feature explanations also reported high trust in this form of explanation. The highest measured explanation satisfaction was observed with the local feature explanation technique in the group of bachelor’s and master’s students, with master’s students additionally expressing high satisfaction with the global feature importance explanation. A detailed overview shows that the two observed groups of students displayed consensus in favored explanation techniques when evaluating trust and explanation satisfaction. Certainty explanation techniques were perceived with lower trust and satisfaction than were local feature relevance explanation techniques. The correlation between itemized results was documented and measured with the Trust in Automation questionnaire and Explanation Satisfaction Scale questionnaire. Master’s-level students self-reported an overall higher understanding of the explanations and higher overall satisfaction with explanations and perceived the explanations as less harmful.
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Weisberg, Deena Skolnick, Frank C. Keil, Joshua Goodstein, Elizabeth Rawson, and Jeremy R. Gray. "The Seductive Allure of Neuroscience Explanations." Journal of Cognitive Neuroscience 20, no. 3 (March 2008): 470–77. http://dx.doi.org/10.1162/jocn.2008.20040.

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Explanations of psychological phenomena seem to generate more public interest when they contain neuroscientific information. Even irrelevant neuroscience information in an explanation of a psychological phenomenon may interfere with people's abilities to critically consider the underlying logic of this explanation. We tested this hypothesis by giving naïve adults, students in a neuroscience course, and neuroscience experts brief descriptions of psychological phenomena followed by one of four types of explanation, according to a 2 (good explanation vs. bad explanation) × 2 (without neuroscience vs. with neuroscience) design. Crucially, the neuroscience information was irrelevant to the logic of the explanation, as confirmed by the expert subjects. Subjects in all three groups judged good explanations as more satisfying than bad ones. But subjects in the two nonexpert groups additionally judged that explanations with logically irrelevant neuroscience information were more satisfying than explanations without. The neuroscience information had a particularly striking effect on nonexperts' judgments of bad explanations, masking otherwise salient problems in these explanations.
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Skorupski, John. "Explanation in the Social Sciences: Explanation and Understanding in Social Science." Royal Institute of Philosophy Supplement 27 (March 1990): 119–34. http://dx.doi.org/10.1017/s1358246100005075.

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Hempelian orthodoxy on the nature of explanation in general, and on explanation in the social sciences in particular, holds that(a) full explanations are arguments(b) full explanations must include at least one law(c) reason explanations are causalDavid Ruben disputes (a) and (b) but he does not dispute (c). Nor does he dispute that ‘explanations in both natural and social science need laws in other ways, even when not as part of the explanation itself (p. 97 above). The distance between his view and the covering law theory, he points out, ‘is not as great as it may first appear to be’ (p. 97 above).
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Swinburne, Richard. "The Limits of Explanation: The Limits of Explanation." Royal Institute of Philosophy Supplement 27 (March 1990): 177–93. http://dx.doi.org/10.1017/s1358246100005105.

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In purporting to explain the occurrence of some event or process we cite the causal factors which, we assert, brought it about or keeps it in being. The explanation is a true one if those factors did indeed bring it about or keep it in being. In discussing explanation I shall henceforward (unless I state otherwise) concern myself only with true explanations. I believe that there are two distinct kinds of way in which causal factors operate in the world, two distinct kinds of causality, and so two distinct kinds of explanation. For historical reasons, I shall call these kinds of causality and explanations ‘scientific’ and ‘personal’; but I do not imply that there is anything unscientific in a wide sense in invoking personal explanation.
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Gillett, Carl. "WHY CONSTITUTIVE MECHANISTIC EXPLANATION CANNOT BE CAUSAL." American Philosophical Quarterly 57, no. 1 (January 1, 2020): 31–50. http://dx.doi.org/10.2307/48570644.

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Abstract In his “New Consensus” on explanation, Wesley Salmon (1989) famously argued that there are two kinds of scientific explanation: global, derivational, and unifying explanations, and then local, ontic explanations backed by causal relations. Following Salmon’s New Consensus, the dominant view in philosophy of science is what I term “neo-Causalism” which assumes that all ontic explanations of singular fact/event are causal explanations backed by causal relations, and that scientists only search for causal patterns or relations and only offer causal explanations of singular facts/events. I argue that there are foundational, and fatal, flaws in the neo-Causal picture. The relations backing constitutive mechanistic explanations of activities of wholes using activities of parts, as well as other species of compositional explanation, cannot be causal relations. Treating them as causal or causation-like is therefore plausibly a category mistake. Compositional explanations in the sciences represent instead a sui generis kind of ontic explanation of singular fact/event backed by sui generis compositional relations. We thus need a pluralistic revision of Salmon’s New Consensus on explanation to reflect these findings.
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Morton, Adam. "Mathematical Modelling and Contrastive Explanation." Canadian Journal of Philosophy Supplementary Volume 16 (1990): 251–70. http://dx.doi.org/10.1080/00455091.1990.10717228.

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This is an enquiry into flawed explanations. Most of the effort in studies of the concept of explanation, scientific or otherwise, has gone into the contrast between clear cases of explanation and clear non-explanations.
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Dissertations / Theses on the topic "Explanation"

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Deulofeu, Batllori Roger. "Scientific explanation in biology. Beyond mechanistic explanation." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/668748.

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Understanding how scientists explain has been one of the major goals of the philosophy of science. Given that explaining is one of the most important tasks that scientists aim at and given the high specialization that currently affects all scientific disciplines, we encounter what might at first glance appear to us as many different types of explanations and very different ways of explaining natural phenomena. This suggests a pluralist picture regarding scientific explanation, particularly in biology, namely the existence of different accounts of explanation that do not share an interesting common core. However, the main goal of the traditional analysis of scientific explanation was to elaborate a monist theory of explanation according to which all scientific explanations share a common core that makes them what they are - i.e. that they can be identified by a commonly shared set of necessary and jointly sufficient conditions. The monist accounts mainly draw on examples from physics to illustrate how this is supposed to work, leaving examples from the special science, like biology, aside. In the last twenty years, nonetheless, the rise of the New Mechanism philosophy, with its notion of mechanistic explanation, has become the dominant and widely accepted account among the philosophers of science to analyze scientific explanation in biology, challenging the pluralist view. The New mechanist account of scientific explanation is essentially monist since their defenders claim that mechanisms are all what really matters to explanation. According to mechanistic explanation, in order to explain a biological phenomenon, we have to discover the mechanism that is responsible for it. Further, we have to decompose this mechanism in order to identify its component parts and identify the causal story that connects the components with the phenomenon. Mechanistic explanations are thus considered causal explanations. The New Mechanism philosophy has arguably been very successful in analyzing how explanation works in a huge diversity of models in biology, suggesting that their account of mechanistic explanation is the only legitimate of in biology. Furthermore, New Mechanism philosophy provides a new framework that contributed to tackle traditional problems of the philosophy of science related to notions such as laws of nature, function, causation, etc. Although mechanistic explanation has proved very successful in analyzing the explanatory force of many biological models, its scope in biology is still under discussion. In the last few years, there has been voices limiting the extension of this account. On the one hand, there has been philosophers claiming that in some biological models, mathematics plays not only a representational role but an explanatory role, suggesting that those models provide explanations that rather than identifying a mechanism with its components and causal story, identify mathematical properties that are explanatory of some phenomenon. They claim that in those explanations, the system under analysis has a mathematical structure whose mathematical properties are explanatory of a particular range of explananda. On the other hand, and despite the claim widely accepted that there are no laws in biology, some philosophers claim we can still consider that some biological models explain by appeal to laws of nature, suggesting covering law accounts of scientific explanation. The present thesis dissertation is a contribution to the aforementioned debate. It provides examples of biological models whose explanatory power does not lie in its identification of mechanisms with its parts and causal story, even if the models look somehow mechanistic. I claim they provide non-mechanistic (and non-causal) explanations, in so far as the models, even if they could identify a mechanism, do not explain by pinpointing information about its causal story.
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Paez, Andres. "Explanations in K : an analysis of explanation as a belief revision operation /." Oberhausen : Athena, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=015470212&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Hegemann, Lena. "Reciprocal Explanations : An Explanation Technique for Human-AI Partnership in Design Ideation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281339.

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Advancements in creative artificial intelligence (AI) are leading to systems that can actively work together with designers in tasks such as ideation, i.e. the creation, development, and communication of ideas. In human group work, making suggestions and explaining the reasoning behind them as well as comprehending other group member’s explanations aids reflection, trust, alignment of goals and inspiration through diverse perspectives. Despite their ability to inspire through independent suggestions, state-of-the-art creative AI systems do not leverage these advantages of group work due to missing or one-sided explanations. For other use cases, AI systems that explain their reasoning are already gathering wide research interest. However, there is a knowledge gap on the effects of explanations on creativity. Furthermore, it is unknown whether a user can benefit from also explaining their contributions to an AI system. This thesis investigates whether reciprocal explanations, a novel technique which combines explanations from and to an AI system, improve the designers’ and AI’s joint exploration of ideas. I integrated reciprocal explanations into an AI aided tool for mood board design, a common method for ideation. In our implementation, the AI system uses text to explain which features of its suggestions match or complement the current mood board. Occasionally, it asks for user explanations providing several options for answers that it reacts to by aligning its strategy. A study was conducted with 16 professional designers who used the tool to create mood boards followed by presentations and semi-structured interviews. The study emphasized a need for explanations that make the principles of the system transparent and showed that alignment of goals motivated participants to provide explanations to the system. Also, enabling users to explain their contributions to the AI system facilitated reflection on their own reasons.
Framsteg inom kreativ artificiell intelligens (AI) har lett till system som aktivt kan samarbeta med designers under idéutformningsprocessen, dvs vid skapande, utveckling och kommunikation av idéer. I grupparbete är det viktigt att kunna göra förslag och förklara resonemanget bakom dem, samt förstå de andra gruppmedlemmarnas resonemang. Detta ökar reflektionsförmågan och förtroende hos medlemmarna, samt underlättar sammanjämkning av mål och ger inspiration genom att höra olika perspektiv. Trots att system, baserade på kreativ artificiell intelligens, har förmågan att inspirera genom sina oberoende förslag, utnyttjar de allra senaste kreativa AI-systemen inte dessa fördelar för att facilitera grupparbete. Detta är på grund av AI-systemens bristfälliga förmåga att resonera över sina förslag. Resonemangen är ofta ensidiga, eller saknas totalt. AI-system som kan förklara sina resonemang är redan ett stort forskningsintresse inom många användningsområden. Dock finns det brist på kunskap om AI-systemens påverkan på den kreativa processen. Dessutom är det okänt om en användare verkligen kan dra nytta av möjligheten att kunna förklara sina designbeslut till ett AI-system. Denna avhandling undersöker om ömsesidiga förklaringar, en ny teknik som kombinerar förklaringar från och till ett AI system, kan förbättra designerns och AI:s samarbete under utforskningen av idéer. Jag integrerade ömsesidiga förklaringar i ett AI-hjälpmedel som underlättar skapandet av stämningsplank (eng. mood board), som är en vanlig metod för konceptutveckling. I vår implementering använder AI-systemet textbeskrivningar för att förklara vilka delar av dess förslag som matchar eller kompletterar det nuvarande stämningsplanket. Ibland ber den användaren ge förklaringar, så den kan anpassa sin förslagsstrategi efter användarens önskemål. Vi genomförde en studie med 16 professionella designers som använde verktyget för att skapa stämningsplank. Feedback samlades genom presentationer och semistrukturerade intervjuer. Studien betonade behovet av förklaringar och resonemang som gör principerna bakom AI-systemet transparenta för användaren. Höjd sammanjämkning mellan användarens och systemets mål motiverade deltagarna att ge förklaringar till systemet. Genom att göra det möjligt för användare att förklara sina designbeslut för AI-systemet, förbättrades också användarens reflektionsförmåga över sina val.
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Almeqdad, Qais Ibrahim. "Self-explanation and explanation in children with learning difficulties." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612344.

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Roberts, Rosemary. "What makes an explanation a good explanation? : adult learners' criteria for acceptance of a good explanation /." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0006/MQ42436.pdf.

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Hörberg, Eric. "Is explanation overrated? : A research on how explanation affects performance." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157513.

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School results are dropping in Sweden and actions are taken by the swedish government to prevent it. This report questions these actions.With a parallel between school and video games, in that they are both about teaching a student/player how to do something, a game is made to test how further explanation of the games mechanics affects the players ability to learn about them. The results are in line with other studies, overexplaining is hurting the players ability to learn about the game.
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Lipton, P. "Explanation and evidence." Thesis, University of Oxford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.371691.

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Bodle, Matthew James. "Grounding and explanation." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/grounding-and-explanation(23a3509e-ffbe-4750-a928-7cb031e0c6de).html.

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This thesis defends the notion of grounding — an explanatory connection of non-causal determination. I present four challenges to developing a systematic theory of grounding, and show that they can be met satisfactorily. The first challenge is that grounding is unintelligible or uninformative—or at any rate, that its work can be done by more familiar notions. If so, the notion of grounding is not even prima facie justified. I argue that grounding is at least as informative as—and, in some respects, more informative than—the more familiar notions it is supposed to supplant. It is necessary because we can express with it certain explanatory relation-ships which are just inexpressible with weaker notions of determination. My defence of grounding is preferable to extant defences since it is less concessive, requiring fewer assumptions about the nature of grounding. A key motivation for grounding is that it is an explanatory connection. The second challenge is that the sense in which grounding is a (distinctly) explanatory relation is unclear, wherefore the case for grounding is severely weakened. I motivate a theory of explanation and argue that it comports nicely with the sense in which grounding is explanatory. Moreover, I characterise a new explanatory notion I call philosophical ex-planation with grounding at its core. This notion illustrates the importance of grounding for philosophical methodology generally. The third challenge is to the internal coherence of grounding theory. A dilemma apparently show that grounding connections can be neither grounded nor ungrounded. Several treatments of this problem already exist, but none is satisfactory. Some imply implausible explanations. Others require new—dubious—posits. I present a new solu-tion, which o ̇ers satisfying explanations but requires no dubious posits. It explains, moreover, why some grounding connections appear to admit of explanation but others do not. The last challenge is to the usefulness of grounding. While it is an interesting meta-metaphysical posit, it o ̇ers little to the metaphysician working on first-order problems. I show how grounding can be fruitfully applied to breaking the deadlock in the debate about laws of nature.
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Nickel, Bernhard Ph D. Massachusetts Institute of Technology. "Truth in explanation." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33711.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 2005.
Includes bibliographical references (p. 155-163).
My thesis consists of three papers on truth and explanations in science. Broadly, the question I ask is semantic. Should the best account of certain bits of our scientific practice focus on the concept of truth? More specifically, should the crucial distinctions between good and bad aspects of that practice be drawn in terms of truth? My thesis consists of three case studies: ceteris paribus laws in the special sciences, appeals to idealizations in the application of theories, and the analysis of explanations quite generally, exemplified in the asymmetry of explanation. In each case, prominent philosophers have argued that a proper treatment does not focus on truth. In each case, I argue that truth should play a central role. And in each case, the issue turns, at least in part, on the connection between the scientific practice in question and explanations.
by Bernhard Nickel.
Ph.D.
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Taylor, Elanor Lycan William G. "Models and explanation." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,1914.

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Thesis (M.A.)--University of North Carolina at Chapel Hill, 2008.
Title from electronic title page (viewed Dec. 11, 2008). "... in partial fulfillment of the requirements for the degree of Master of Arts in the Department of Philosophy." Discipline: Philosophy; Department/School: Philosophy.
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Books on the topic "Explanation"

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1940-, Cornwell John, ed. Explanations: Styles of explanation in science. Oxford: Oxford University Press, 2004.

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Hon, Giora, and Sam S. Rakover, eds. Explanation. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9731-9.

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David-Hillel, Ruben, ed. Explanation. Oxford: Oxford University Press, 1993.

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1958-, Campbell Joseph Keim, O'Rourke Michael 1963-, and Silverstein Harry 1942-, eds. Causation and explanation. Cambridge, Mass: MIT Press, 2007.

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Weber, Erik, Jeroen Van Bouwel, and Leen De Vreese. Scientific Explanation. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6446-0.

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PERSSON, JOHANNES, and PETRI YLIKOSKI, eds. RETHINKING EXPLANATION. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5581-2.

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1947-, Kitcher Philip, and Salmon Wesley C, eds. Scientific explanation. Minneapolis: University of Minnesota Press, 1989.

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Theophylactus. The explanation. House Springs, Mo: Chysostom Press, 1992.

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Johannes, Persson, and Ylikoski Petri, eds. Rethinking explanation. Dordrecht: Springer, 2007.

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Ruben, David-Hillel. Explaining explanation. London: Routledge, 1990.

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Book chapters on the topic "Explanation"

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Hon, Giora. "The Why and How of Explanation: An Analytical Exposition." In Explanation, 1–39. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9731-9_1.

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Salmon, Merrilee H. "Explanation in Archaeology." In Explanation, 231–48. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9731-9_10.

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Uttal, William R. "Can Psychological Processes be Explained? A Call for a Revitalized Behaviorism." In Explanation, 251–75. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9731-9_11.

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Balaban, Oded. "The Use of Error as an Explanatory Category in Politics." In Explanation, 277–306. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9731-9_12.

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Lorand, Ruth. "Are There Aesthetic Explanations?" In Explanation, 307–25. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9731-9_13.

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Lipton, Peter. "What Good is an Explanation?" In Explanation, 43–59. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9731-9_2.

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Salmon, Wesley C. "Explanation and Confirmation: A Bayesian Critique of Inference to the Best Explanation." In Explanation, 61–91. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9731-9_3.

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Lipton, Peter. "Is Explanation a Guide to Inference? A Reply to Wesley C. Salmon." In Explanation, 93–120. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9731-9_4.

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Salmon, Wesley C. "Reflections of a Bashful Bayesian: A Reply to Peter Lipton." In Explanation, 121–36. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9731-9_5.

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Harari-Eshel, Orna. "Knowledge and Explanation in Aristotle’s Posterior Analytics." In Explanation, 137–64. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9731-9_6.

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Conference papers on the topic "Explanation"

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Yepmo, Véronne. "Anomaly Explanation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/844.

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With the surge of deep learning and laws aiming at regulating the use of artificial intelligence, providing explanations to algorithms outputs has been a hot topic in the recent years. Most works are devoted to the explanation of classifiers outputs. The explanation of unsupervised machine learning algorithms, like anomaly detection, has received less attention from the XAI community. But this little interest is not imputable to the irrelevance of the topic. In this paper, we demonstrate the importance of anomaly explanation, the areas still needing investigation based upon our previous contributions to the field, and the future directions that will be explored.
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Chowdhury, Tanya, Razieh Rahimi, and James Allan. "Equi-explanation Maps: Concise and Informative Global Summary Explanations." In FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3531146.3533112.

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Mahmud, Saaduddin, Sandhya Saisubramanian, and Shlomo Zilberstein. "Explanation-Guided Reward Alignment." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/53.

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Agents often need to infer a reward function from observations to learn desired behaviors. However, agents may infer a reward function that does not align with the original intent because there can be multiple reward functions consistent with its observations. Operating based on such misaligned rewards can be risky. Furthermore, black-box representations make it difficult to verify the learned rewards and prevent harmful behavior. We present a framework for verifying and improving reward alignment using explanations and show how explanations can help detect misalignment and reveal failure cases in novel scenarios. The problem is formulated as inverse reinforcement learning from ranked trajectories. Verification tests created from the trajectory dataset are used to iteratively validate and improve reward alignment. The agent explains its learned reward and a tester signals whether the explanation passes the test. In cases where the explanation fails, the agent offers alternative explanations to gather feedback, which is then used to improve the learned reward. We analyze the efficiency of our approach in improving reward alignment using different types of explanations and demonstrate its effectiveness in five domains.
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Chakraborti, Tathagata, Sarath Sreedharan, Yu Zhang, and Subbarao Kambhampati. "Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/23.

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When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.
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Srinivasan, Ramya, and Ajay Chander. "Explanation Perspectives from the Cognitive Sciences---A Survey." 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.2020/670.

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With growing adoption of AI across fields such as healthcare, finance, and the justice system, explaining an AI decision has become more important than ever before. Development of human-centric explainable AI (XAI) systems necessitates an understanding of the requirements of the human-in-the-loop seeking the explanation. This includes the cognitive behavioral purpose that the explanation serves for its recipients, and the structure that the explanation uses to reach those ends. An understanding of the psychological foundations of explanations is thus vital for the development of effective human-centric XAI systems. Towards this end, we survey papers from the cognitive science literature that address the following broad questions: (1) what is an explanation, (2) what are explanations for, and 3) what are the characteristics of good and bad explanations. We organize the insights gained therein by means of highlighting the advantages and shortcomings of various explanation structures and theories, discuss their applicability across different domains, and analyze their utility to various types of humans-in-the-loop. We summarize the key takeaways for human-centric design of XAI systems, and recommend strategies to bridge the existing gap between XAI research and practical needs. We hope this work will spark the development of novel human-centric XAI systems.
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Bairy, Akhila, and Martin Fränzle. "Optimal Explanation Generation using Attention Distribution Model." In 9th International Conference on Human Interaction and Emerging Technologies - Artificial Intelligence and Future Applications. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002928.

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With highly automated and Autonomous Vehicles (AVs) being one of the most prominent emerging technologies in the automotive industry, efforts to achieve SAE Level 3+ vehicles have skyrocketed in recent years. As new technologies emerge on a daily basis, these systems are becoming increasingly complex. To help people understand - and also accept - these new technologies, there is a need for explanation. There are three essential dimensions to designing explanations, namely content, frequency, and timing. Our goal is to develop an algorithm that optimises explanation in AVs. Most of the existing research focuses on the content of an explanation, whereas the fine-granularity of the frequency and timing of an explanation is relatively unexplored. Previous studies concerning "when to explain" have tended to make broad distinctions between explaining before, during or after an action is performed. For AVs, studies have shown that passengers prefer to receive an explanation before an autonomous action takes place. However, it seems likely that the acclimatisation that occurs through prolonged exposure to and use of a particular AV will reduce the need for explanation. As comprehension of explanations is workload-intensive, it is necessary to optimise both the frequency, i.e. skipping explanations when they are not helpful to reduce workload, and the precise point in time when an explanation is given, i.e. giving an explanation when it provides the maximum workload reduction. Extra mental workload for passengers can be caused by both giving and omitting an explanation. Every explanation that is presented requires cognitive processing in order to be understood, even if its content is considered to be redundant or if it will not be remembered by the addressee. On the other hand, skipping the explanation can cause the passenger to actively scan the environment for potential cues themselves, if necessary. Such an attention strategy would also impose a significant cognitive load on the passenger. In our work, to predict the mental workload of the passenger, we use the state-of-the-art attention model called SEEV (Salience, Effort, Expectancy, and Value). The SEEV model is dynamically used for forecasting the likelihood of the direction of attention. Our work aims to generate an optimally timed strategy for presenting an explanation. Using the SEEV model we build a probabilistic reactive game, i.e., 1.5-player game or Markov Decision Process, and we use reactive synthesis to generate an optimal reactive strategy for presenting an explanation that minimises workload.
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Bhatt, Umang, Adrian Weller, and José M. F. Moura. "Evaluating and Aggregating Feature-based Model 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.2020/417.

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A feature-based model explanation denotes how much each input feature contributes to a model's output for a given data point. As the number of proposed explanation functions grows, we lack quantitative evaluation criteria to help practitioners know when to use which explanation function. This paper proposes quantitative evaluation criteria for feature-based explanations: low sensitivity, high faithfulness, and low complexity. We devise a framework for aggregating explanation functions. We develop a procedure for learning an aggregate explanation function with lower complexity and then derive a new aggregate Shapley value explanation function that minimizes sensitivity.
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Bhavya and ChengXiang Zhai. "Explanation Mining." In L@S '20: Seventh (2020) ACM Conference on Learning @ Scale. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3386527.3406738.

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Peake, Georgina, and Jun Wang. "Explanation Mining." In KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219819.3220072.

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Baraldi, Andrea, Francesco Del Buono, Matteo Paganelli, and Francesco Guerra. "Landmark Explanation." In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459637.3481981.

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Reports on the topic "Explanation"

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Santos, Jr, and Eugene. Modelling Temporal Abductive Explanation. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada263096.

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VanLehn, Kurt, William Ball, and Bernadette Kowalski. Explanation-Based Learning of Correctness: Towards a Model of the Self-Explanation Effect. Fort Belvoir, VA: Defense Technical Information Center, May 1990. http://dx.doi.org/10.21236/ada225644.

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Ford, Nicholas, and Charles Yuji Horioka. The 'Real' Explanation of the PPP Puzzle. Cambridge, MA: National Bureau of Economic Research, April 2016. http://dx.doi.org/10.3386/w22198.

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Rosenbloom, Paul S., Soowon Lee, and Amy Unruh. Bias in Planning and Explanation-Based Learning. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada269608.

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Feiner, Steven K., and Kathleen R. McKeown. Coordinating Text and Graphics in Explanation Generation. Fort Belvoir, VA: Defense Technical Information Center, January 1989. http://dx.doi.org/10.21236/ada460217.

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VanLehn, Kirt, Randolph M. Jones, and Michelene T. Chi. A Model of the Self-Explanation Effect. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada241200.

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Schulz, Jan, Daniel Mayerhoffer, and Anna Gebhard. A Network-Based Explanation of Perceived Inequality. Otto-Friedrich-Universität, 2021. http://dx.doi.org/10.20378/irb-49393.

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Across income groups and countries, the public perception of economic inequality and many other macroeconomic variables such as inflation or unemployment rates is spectacularly wrong. These misperceptions have far-reaching consequences, as it is perceived inequality, not actual inequality informing redistributive preferences. The prevalence of this phenomenon is independent of social class and welfare regime, which suggests the existence of a common mechanism behind public perceptions. We propose a network-based explanation of perceived inequality building on recent advances in random geometric graph theory. The literature has identified several stylised facts on how individual perceptions respond to actual inequality and how these biases vary systematically along the income distribution. Our generating mechanism can replicate all of them simultaneously. It also produces social networks that exhibit salient features of real-world networks; namely, they cannot be statistically distinguished from small-world networks, testifying to the robustness of our approach. Our results, therefore, suggest that homophilic segregation is a promising candidate to explain inequality perceptions with strong implications for theories of consumption behaviour.
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Wang, Pengfei, and Yi Wen. Financial Development and Economic Volatility: A Unified Explanation. Federal Reserve Bank of St. Louis, 2009. http://dx.doi.org/10.20955/wp.2009.022.

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Ford, Nicholas, and Charles Yuji Horioka. The "Real" Explanation of the Feldstein-Horioka Puzzle. Cambridge, MA: National Bureau of Economic Research, March 2016. http://dx.doi.org/10.3386/w22081.

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Rathke, Christian, and David F. Redmiles. Multiple Representation Perspectives for Supporting Explanation in Context. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada447671.

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