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Traditionally, nature has often been thought to be structured by essential forms providing the generic features of natural things and thus the foundations for scientific explanations. In contrast, human history and the social domain have been thought to be the realm of ever‐changing appearances, where contingency prevails. The paper argues that the existence of essential forms is compatible with the contingent, mind‐dependent and historical character of the social world, and that essential forms can also be found in the social domain. Two categories of entities are discussed that suggest themselves to be identified as social forms, namely social kinds and social identities. To this end, it is shown that standard arguments for the existence of essential forms also apply to the social domain, and objections to the existence of social forms are rebutted. Particular attention is paid to the explanatory appeal of social forms. The paper concludes by suggesting that, far from being oppressive, essential social forms are presupposed by liberating social practices. [ABSTRACT FROM AUTHOR]

Medical training is a key element in maintaining and improving today's healthcare standards. Given the nature of medical work, students must master not only theory but also develop their hands-on abilities and skills in clinical practice. Medical simulators play an increasing role in supporting the active learning of these students due to their ability to present a large variety of tasks allowing students to train and experiment indefinitely without causing any patient harm. While the criticality of explainable AI systems has been extensively discussed in the literature, the medical training context presents unique user needs for explanations. In this paper, we explore the potential gap of current limitations within simulation-based training, and the role Artificial Intelligence (AI) holds in supporting the needs of medical students in training. Through contextual inquiries and interviews with clinicians in training (N = 9) and subsequent validation with medical experts (N = 4), we obtain an understanding of the shortcomings in current simulation-based training and offer recommendations for future AI-driven training. Our results stress the need for continuous and actionable feedback that resembles the interaction between clinical supervisor and resident in real-world training scenarios while adjusting training material to the residents' skills and prior performance. [ABSTRACT FROM AUTHOR]

The paper examines teacher pragmatic acts (verbal and embodied) in response to children's crying in a Russian preschool. It focuses on crying episodes associated with children's inability or unwillingness to follow the norms and expectations surrounding children's conduct (e.g., crying when not allowed to do something). The paper employs discourse analysis; the data are 40 h of video- and audio-recorded interactions. The analysis shows that teachers used directives and other pragmatic acts (e.g., assessments, explanations) that encouraged children to stop crying. The directives were produced in multi-party participation frameworks where peers were referred to or recruited as active participants. Teachers used positive and negative assessments of crying. Crying was presented in a negative light in contrast with positive non-crying behaviors; positive assessments (praise and affectionate touch) were offered when crying stopped. The consequences of crying were presented as negatively affecting others. It is argued that such discursive treatment of crying contributes to socializing children to conformity. The paper contributes to our understanding of how through the use of pragmatic acts, child crying is discursively treated in ways that invoke norms of pragmatic conduct (e.g., not crying when asked to participate in a planned activity) and social values regarding group membership. • Analysis examines teachers' pragmatic acts (verbal and embodied) in response to crying in a Russian preschool. • Methods utilize discourse analysis. • Findings explore teachers' use of directive trajectories and other pragmatic acts that encouraged children to stop crying. • Conclusions are discussed in relation to norms of pragmatic conduct and social values. [ABSTRACT FROM AUTHOR]

Leibniz is often cited as an authority when it comes to the formulation and answer strategy of the question "Why is there something rather than nothing?" Yet much current research assumes that Leibniz advocates an unambiguous question and strategy for the answer. In this respect, one repeatedly finds the argument in the literature that alternative explanatory approaches to this question violate Leibniz's intention, since he derives the question from the principle of sufficient reason and also demands a causal explanation to the question. In particular, the new research on non-causal explanatory strategies to the Leibniz question seems to concern this counter-argument. In this paper, however, I will argue that while Leibniz raises the question by means of the principle of sufficient reason, he even favours a non-causal explanatory strategy to the question. Thus, a more accurate Leibniz interpretation seems not only to legitimise but also to support non-causal explanations to the Leibniz question. [ABSTRACT FROM AUTHOR]

In this paper, we explore different possible explanations for research misconduct (especially falsification and fabrication), and investigate whether they are compatible. We suggest that to explain research misconduct, we should pay attention to three factors: (1) the beliefs and desires of the misconductor, (2) contextual affordances, (3) and unconscious biases or influences. We draw on the three different narratives (individual, institutional, system of science) of research misconduct as proposed by Sovacool to review six different explanations. Four theories start from the individual: Rational Choice theory, Bad Apple theory, General Strain Theory and Prospect Theory. Organizational Justice Theory focuses on institutional factors, while New Public Management targets the system of science. For each theory, we illustrate the kinds of facts that must be known in order for explanations based on them to have minimal plausibility. We suggest that none can constitute a full explanation. Finally, we explore how the different possible explanations interrelate. We find that they are compatible, with the exception of explanations based on Rational Choice Theory and Prospect Theory respectively, which are incompatible with one another. For illustrative purposes we examine the case of Diederik Stapel. [ABSTRACT FROM AUTHOR]

The offline evaluation of recommender systems is typically based on accuracy metrics such as the Mean Absolute Error and the Root Mean Squared Error for error rating prediction and Precision and Recall for measuring the quality of the top-N recommendations. However, it is difficult to reproduce the results since there are various libraries that can be used for running experiments and also within the same library there are many different settings that if not taken into consideration when replicating the results might vary. In this paper, we show that within the use of the same library an explanation-based approach can be used to assist in the reproducibility of experiments. Our proposed approach has been experimentally evaluated using a wide range of recommendation algorithms ranging from collaborative filtering to complicated fuzzy recommendation approaches that can solve the filter bubble problem, a real dataset, and the results show that it is both practical and effective. [ABSTRACT FROM AUTHOR]

• Derivation of an alternative method for the interpretation of deep learning models. • Design a human-expert level deep learning diabetic retinopathy classification model. • Design a diabetic retinopathy interpretable classifier. • Visualization of typical feature and input space score map samples. In this paper we present a diabetic retinopathy deep learning interpretable classifier. On one hand, it classifies retina images into different levels of severity with good performance. On the other hand, this classifier is able of explaining the classification results by assigning a score for each point in the hidden and input spaces. These scores indicate the pixel contribution to the final classification. To obtain these scores, we propose a new pixel-wise score propagation model that for every neuron, divides the observed output score into two components. With this method, the generated visual maps can be easily interpreted by an ophthalmologist in order to find the underlying statistical regularities that help to the diagnosis of this eye disease. [ABSTRACT FROM AUTHOR]

People often offer an excuse or an apology after they do something wrong. In this paper, we examine how giving an excuse, an apology, or no explanation after arriving late to a meeting influences the attitudes and behavioral intentions others form toward the late arrival. Additionally, we examined how a group-related factor (complaining) and the late arrival's history with coming late affected participant judgments. Across two studies using complementary experimental and survey methods, we found that an excuse is better than no explanation, but that the difference between apology and no explanation and apology and excuse is not always clear. Furthermore, we found that common distinctions between explanation types used in the literature may not fully exist in non-laboratory social interactions. Implications of these findings and future directions are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present the formalization and implementation of a decision framework in which a group of agents can express their preferences among a set of alternatives by using several comparison criteria. We define special kind of argument to support agents' preferences, and the set of generated arguments is used both to obtain the set of selected alternatives and to explain why they were selected. We introduce a software application that implements our proposed approach and provides interactive tools for obtaining different kinds of explanations for the computed results. Also, we formally study the proposed argumentation process and we prove that the set of warranted conclusions, which support the obtained results, is consistent. We also show that our framework works properly on choice scenarios with incomplete evidence, and we propose how to optimize the computation process for dynamic environments. [ABSTRACT FROM AUTHOR]

Dung's Argumentation Framework (AF) has been extended in several directions, including the possibility of representing uncertainty about the existence of arguments and attacks. In this regard, two main proposals have been introduced in the literature: Probabilistic Argumentation Framework (PrAF) and Incomplete Argumentation Framework (iAF). PrAF is an extension of AF with probability theory, thus representing quantified uncertainty. In contrast, iAF represents unquantified uncertainty, that is it can be seen as a special case where we only know that some elements (arguments or attacks) are uncertain. In this paper, we first address the problem of computing the probability that a given argument is accepted in PrAF. This is carried out by introducing the concept of probabilistic explanation for any given (probabilistic) extension. We show that the complexity of the problem is FP # P -hard and propose polynomial approximation algorithms with bounded additive error for PrAFs where odd-length cycles are forbidden. We investigate the approximate complexity of the related FP # P -hard problems of credulous and skeptical acceptance in PrAF, showing that they are generally harder than the problem of computing the probability that a given argument is accepted. Next we consider iAF and, after showing some equivalence properties among classes of iAFs, we study iAF as a special case of PrAF where uncertain elements have associated a probability equal to 1/2. Finally, given this result, we investigate the relationships between iAF acceptance problems and probabilistic acceptance in PrAF. [ABSTRACT FROM AUTHOR]

Highlights • Decision support system for applications with multiple comparison criteria. • Users' preferences configurable via conditional rules. • Decision framework based on an argumentative system. • The system provides detailed explanations for the selected alternatives. • Empirical analysis to evaluate the performance of an implemented version. Abstract In our current society, it is very common to have access to a huge amount of information and scenarios in which it is possible to choose among a great number of alternatives. Therefore, systems that automate or assist with the decision-making process are becoming crucial. Those systems should allow users to represent their preferences accurately, even in scenarios where the user has many forms of comparing the available alternatives. In this paper, we propose an argument-based framework for multi-criteria decision-making based on conditional rules, that allows users to encode their preferences among the comparison criteria in terms of requirement and tolerance values. From these rules, arguments that are involved in the comparison process are generated. In order to comply with users' demands for explanations of the given answers, our proposed framework can provide qualitative explanations about the argumentation process, and also an explanatory graph that shows how all the alternatives were ranked and the connections with the user's rules that were used. We also show the results of an empirical analysis of the performance of a developed prototype. [ABSTRACT FROM AUTHOR]

Constructing explanatory models, in which students learn to visualize the mechanisms of unobservable entities (e.g., molecules) to explain the working of observable phenomena (e.g., air pressure), is a key practice of science. Yet, students struggle to develop and utilize such models to articulate causal‐mechanistic explanations. In this paper, we argue that representational gesturing with the hands (i.e., gesturing that models semantic content) can support the development of explanatory models. Through case studies examining middle school students gesturing during sensemaking, we show that representational gestures can support students in at least four ways: (a) they make underlying mechanisms visible, (b) they facilitate translation of a spatial model to a verbal explanation, (c) they enable model articulation while relying less on scientific terminology, and (d) they present opportunities for students to embody causal agents. In these ways, representational gesturing can be considered an epistemic tool supporting students during sensemaking and communication. We argue that instruction should attend to students' gestures and, as appropriate, encourage students to gesture as a means of aiding the construction and articulation of causal‐mechanistic explanations. While our study explores one form of embodied representation, we encourage the field to explore embodied expressions as epistemic tools for learning. [ABSTRACT FROM AUTHOR]

In this paper, we present a fundamental framework for defining different types of explanations of AI systems and the criteria for evaluating their quality. Starting from a structural view of how explanations can be constructed, i.e., in terms of an explanandum (what needs to be explained), multiple explanantia (explanations, clues, or parts of information that explain), and a relationship linking explanandum and explanantia, we propose an explanandum-based typology and point to other possible typologies based on how explanantia are presented and how they relate to explanandia. We also highlight two broad and complementary perspectives for defining possible quality criteria for assessing explainability: epistemological and psychological (cognitive). These definition attempts aim to support the three main functions that we believe should attract the interest and further research of XAI scholars: clear inventories, clear verification criteria, and clear validation methods. • We propose a framework for defining different types of explanations of AI systems. • We contextualize current XAI discourses within the proposed framework. • We highlight two broad perspectives for defining quality criteria for explainability. • We discuss the relevance of our framework in light of current and upcoming AI regulation. • We confer fundamental aspects for future research of XAI scholars. [ABSTRACT FROM AUTHOR]

The image of economics got somewhat puzzling after the crisis of 2008. Many economists now doubt that economics is able to provide answers to some of its core questions. The crisis was not so fun for economics. However, this not so fun image of economics is not the only image in the eyes of the general public. When one looks at economics-made-fun (EMF) books (e.g. Freakonomics, The Undercover Economist, etc.), economics seems to be an explanatory science which is able to provide interesting, unconventional, entertaining and enlightening explanations for almost every aspect of our lives. Isn't there a great contradiction between these two images of economics? Not necessarily. The present paper explicates why. Nevertheless, the paper also shows that EMF books run the risk of creating a false sense of understanding and explains how one should read the basic insights provided by EMF books to remove this risk. The paper contrasts the EMF version of the explanation of the effects of mandatory seat belt laws with actual research concerning the subject to illustrate its arguments. [ABSTRACT FROM PUBLISHER]

This paper addresses the theoretical notion of a game as it arises across scientific inquiries, exploring its uses as a technical and formal asset in logic and science versus an explanatory mechanism. While games comprise a widely used method in a broad intellectual realm (including, but not limited to, philosophy, logic, mathematics, cognitive science, artificial intelligence, computation, linguistics, physics, economics), each discipline advocates its own methodology and a unified understanding is lacking. In the first part of this paper, a number of game theories in formal studies are critically surveyed. In the second part, the doctrine of games as explanations for logic is assessed, and the relevance of a conceptual analysis of games to cognition discussed. It is suggested that the notion of evolution plays a part in the game-theoretic concept of meaning. [ABSTRACT FROM AUTHOR]

The pervasive use of computer simulations in the sciences brings novel epistemological issues discussed in the philosophy of science literature since about a decade. Evolutionary biology strongly relies on such simulations, and in relation to it there exists a research program (Artificial Life) that mainly studies simulations themselves. This paper addresses the specificity of computer simulations in evolutionary biology, in the context (described in Sect. 1) of a set of questions about their scope as explanations, the nature of validation processes and the relation between simulations and true experiments or mathematical models. After making distinctions, especially between a weak use where simulations test hypotheses about the world, and a strong use where they allow one to explore sets of evolutionary dynamics not necessarily extant in our world, I argue in Sect. 2 that (weak) simulations are likely to represent in virtue of the fact that they instantiate specific features of causal processes that may be isomorphic to features of some causal processes in the world, though the latter are always intertwined with a myriad of different processes and hence unlikely to be directly manipulated and studied. I therefore argue that these simulations are merely able to provide candidate explanations for real patterns. Section 3 ends up by placing strong and weak simulations in Levins' triangle, that conceives of simulations as devices trying to fulfil one or two among three incompatible epistemic values (precision, realism, genericity). [ABSTRACT FROM AUTHOR]

This paper provides an extensive overview of the use of knowledge graphs in the context of Explainable Machine Learning. As of late, explainable AI has become a very active field of research by addressing the limitations of the latest machine learning solutions that often provide highly accurate, but hardly scrutable and interpretable decisions. An increasing interest has also been shown in the integration of Knowledge Representation techniques in Machine Learning applications, mostly motivated by the complementary strengths and weaknesses that could lead to a new generation of hybrid intelligent systems. Following this idea, we hypothesise that knowledge graphs, which naturally provide domain background knowledge in a machine-readable format, could be integrated in Explainable Machine Learning approaches to help them provide more meaningful, insightful and trustworthy explanations. Using a systematic literature review methodology we designed an analytical framework to explore the current landscape of Explainable Machine Learning. We focus particularly on the integration with structured knowledge at large scale, and use our framework to analyse a variety of Machine Learning domains, identifying the main characteristics of such knowledge-based, explainable systems from different perspectives. We then summarise the strengths of such hybrid systems, such as improved understandability, reactivity, and accuracy, as well as their limitations, e.g. in handling noise or extracting knowledge efficiently. We conclude by discussing a list of open challenges left for future research. [ABSTRACT FROM AUTHOR]

This paper shows the research results obtained with a group of pre-service chemistry teachers at Universidad Pedagógica Nacional, employing didactic units focused in everyday chemistry as strategy for chemistry teaching. The explanations of pre-service chemistry teachers for the analyzed everyday chemical phenomena were characterized according to its admissibility and chemical levels of representation. The project allowed consolidating a space which employs coherently disciplinary and didactic aspects of chemistry, contributing to the pre-service teachers with resources and methodologies to transform the traditional forms of teaching, learning and evaluation of chemistry. [ABSTRACT FROM AUTHOR]

Abstract: In decision-making problems under uncertainty, a decision table consists of a set of attributes indicating what is the optimal decision (response) within the different scenarios defined by the attributes. We recently introduced a method to give explanations of these responses. In this paper, the method is extended. To do this, it is combined with a query system to answer expert questions about the preferred action for a given instantiation of decision table attributes. The main difficulty is to accurately answer queries associated with incomplete instantiations. Incomplete instantiations are the result of the evaluation of a partial model outputting decision tables that only include a subset of the whole problem, leading to uncertain responses. Our proposal establishes an automatic and interactive dialogue between the decision-support system and the expert to elicit information from the expert to reduce uncertainty. Typically, the process involves learning a Bayesian network structure from a relevant part of the decision table and computing some interesting conditional probabilities that are revised accordingly. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims to explain previously described increases in self-reported psychological distress between 1987 and 2006 among samples identical in respect of age (15 years), school year and geographical location (West of Scotland). Such increases might be explained by changes in exposure (changes in levels of risk or protective factors) and/or by changes in vulnerability (changes in the relationship between risk/protective factors and psychological distress). Key areas of social change over this time period allow identification of potential explanatory factors, categorised as economic, family, educational, values and lifestyle and represented by variables common to each study. Psychological distress was measured via the 12-item General Health Questionnaire, Likert scored. Analyses were conducted on those with complete data on all variables (N = 3276 of 3929), and separately for males and females. Between 1987 and 2006, levels of almost every potential explanatory factor changed in line with general societal trends. Associations between explanatory factors and GHQ tended to be stronger among females, and at the later date. The strongest associations were with worries, arguments with parents, and, at the later date, school disengagement. The factors which best accounted for the increase in mean GHQ between 1987 and 2006 were arguments with parents, school disengagement, worry about school and, for females, worry about family relationships, reflecting both increasing exposure and vulnerability to these risk factors. A number of limitations to our analysis can be identified. However, our results reinforce the conclusions of others in highlighting the role of family and educational factors as plausible explanations for increases in young people’s psychological distress. [Copyright &y& Elsevier]

Abstract: Network security tests should be periodically conducted to detect vulnerabilities before they are exploited. However, analysis of testing results is resource intensive with many data and requires expertise because it is an unsupervised domain. This paper presents how to automate and improve this analysis through the identification and explanation of device groups with similar vulnerabilities. Clustering is used for discovering hidden patterns and abnormal behaviors. Self-organizing maps are preferred due to their soft computing capabilities. Explanations based on anti-unification give comprehensive descriptions of clustering results to analysts. This approach is integrated in Consensus, a computer-aided system to detect network vulnerabilities. [Copyright &y& Elsevier]

Contrary to common views that philosophy is extraneous to cognitive science, this paper argues that philosophy has a crucial role to play in cognitive science with respect to generality and normativity. General questions include the nature of theories and explanations, the role of computer simulation in cognitive theorizing, and the relations among the different fields of cognitive science. Normative questions include whether human thinking should be Bayesian, whether decision making should maximize expected utility, and how norms should be established. These kinds of general and normative questions make philosophical reflection an important part of progress in cognitive science. Philosophy operates best, however, not with a priori reasoning or conceptual analysis, but rather with empirically informed reflection on a wide range of findings in cognitive science. [ABSTRACT FROM AUTHOR]

Abstract: The goal of predictive toxicology is the automatic construction of carcinogenecity models. Most common artificial intelligence techniques used to construct these models are inductive learning methods. In a previous work we presented an approach that uses lazy learning methods for solving the problem of predicting carcinogenecity. Lazy learning methods solve new problems based on their similarity to already solved problems. Nevertheless, a weakness of these kind of methods is that sometimes the result is not completely understandable by the user. In this paper we propose an explanation scheme for a concrete lazy learning method. This scheme is particularly interesting to justify the predictions about the carcinogenesis of chemical compounds. In addition we propose that these explanations could be used to build a partial domain knowledge. In our particular case, we use the explanations for building general knowledge about carcinogenesis. [Copyright &y& Elsevier]

The paper has three aims. First, to show that Julian Reiss' critique of what he calls the New Mechanist Perspective in the social sciences is built on a number of misconceptions; second, to provide some arguments for the need of reflections and discussions about common and "ultimate" goals for the social sciences; and third, to suggest a focus on mechanisms as one such viable goal. [ABSTRACT FROM AUTHOR]

The research presented here explores the hypothesis that the deployment and acceptance of decision support systems in medicine will be enhanced if the basis for the recommendation produced by the system is apparent. We describe a decision support system for advising on patients suffering from bronchiolitis. This system supports its recommendations with precedent cases selected to support the recommendation along with justification text that highlights aspects of these cases relevant to the query case. It also presents an estimate of its confidence in the recommendation. The main contribution of this paper is an evaluation of this system in a clinical context. The evaluation shows that this type of explanation does enhance the usefulness of the system for practitioners. [ABSTRACT FROM AUTHOR]

This paper comprises results referred to forms of explanation and alternative conceptions identified regarding the Human Immunodeficiency Virus (HIV) and the Acquired Inmunedeficiency Syndrome (AIDS). These results are part of a bigger investigation in 114 biology teachers of secondary education in Mexico City. Most of the identified explanations are descriptive, though teleonomic and anthropomorphic explanations were also found, but in smaller numbers. Some of the main results regarding HIV are: 34 % of the teachers have acceptable conceptions about HIV transmission, however, 56 % of the teachers have alternative conceptions. Finally, 9.6 % of the teachers prefer not to work with this topic. [ABSTRACT FROM AUTHOR]

In this paper, we examine the instructional discourse of science lessons in two primary classrooms for explanations of bird adaptations for flight. We draw on case study data to describe ways in which student construction of explanations is scaffolded by the teachers. We recognised three categories of explanations developed in the discourse: descriptive explanations, relational explanations and explanatory models. Descriptive explanations predominated. We argue that teachers cannot assume that, although the construction of oral explanations in science lessons may move from descriptive to relational or explanatory models, students will be able to write such explanations without specific attention to text structure. [ABSTRACT FROM AUTHOR]

Most of the algorithms developed within the Constraint Satisfaction Problem (CSP) framework cannot be used as such to solve interactive decision support problems, like product configuration. Indeed, in such problems, the user is in charge of assigning values to variables. Global consistency maintaining is only one among several functionalities that should be offered by a CSP-based platform in order to help the user in her task; other important functionalities include providing explanations for some user''s choices and ways to restore consistency.This paper presents an extension of the CSP framework in this direction. The key idea consists in considering and handling the user''s choices as assumptions.  From a theoretical point of view, the complexity issues of various computational tasks involved in interactive decision support problems are investigated. The results cohere with what is known when Boolean constraints are considered and show all the tasks intractable in the worst case. Since interactivity requires short response times, intractability must be circumvented some way. To this end, we present a new method for compiling configuration problems, that can be generalized to valued CSPs. Specifically, an automaton representing the set of solutions of the CSP is first computed off-line, then this data structure is exploited so as to ensure both consistency maintenance and computation of maximal consistent subsets of user''s choices in an efficient way. [Copyright &y& Elsevier]

Research in the social sciences has shown that expectations are an important factor in explanations as used between humans: rather than explaining the cause of an event per se, the explainer will often address another event that did not occur but that the explainee might have expected. For AI-powered systems, this finding suggests that explanation-generating systems may need to identify such end user expectations. In general, this is a challenging task, not the least because users often keep them implicit; there is thus a need to investigate the importance of such an ability. In this paper, we report an empirical study with 181 participants who were shown outputs from a text classifier system along with an explanation of why the system chose a particular class for each text. Explanations were both factual , explaining why the system produced a certain output or counterfactual , explaining why the system produced one output instead of another. Our main hypothesis was explanations should align with end user expectations; that is, a factual explanation should be given when the system's output is in line with end user expectations, and a counterfactual explanation when it is not. We find that factual explanations are indeed appropriate when expectations and output match. When they do not, neither factual nor counterfactual explanations appear appropriate, although we do find indications that our counterfactual explanations contained at least some necessary elements. Overall, this suggests that it is important for systems that create explanations of AI systems to infer what outputs the end user expected so that factual explanations can be generated at the appropriate moments. At the same time, this information is, by itself, not sufficient to also create appropriate explanations when the output and user expectations do not match. This is somewhat surprising given investigations of explanations in the social sciences, and will need more scrutiny in future studies. [ABSTRACT FROM AUTHOR]

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these " stakeholders' desiderata ") in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches. [ABSTRACT FROM AUTHOR]

Information systems with an "intelligent" or "knowledge" component are now prevalent and include knowledge-based systems, decision support systems, intelligent agents, and knowledge management systems. These systems are in principle capable of explaining their reasoning or justifying their behavior. There appears to be a lack of under, standing, however, of the benefits that can flow from explanation use, and how an explanation function should be constructed. Work with newer types of intelligent systems and help functions for everyday systems, such as word-processors, appears in many cases to neglect lessons learned in the past. This paper attempts to rectify this situation by drawing together the considerable body of work on the nature and use of explanations. Empirical studies, mainly with knowledge-based systems, are reviewed and linked to a sound theoretical base. The theoretical base combines a cognitive effort perspective, cognitive learning theory, and Toulmin's model of argumentation. Conclusions drawn from the review have both practical and theoretical significance. Explanations are important to users in a number of circumstances--when the user perceives an anomaly, when they want to learn, or when they need a specific piece of knowledge to participate properly in problem solving. Explanations, when suitably designed, have been shown to improve performance and learning and result in more positive user perceptions of a system. The design is important, however, because it appears that explanations will not be used if the user has to exert "too much" effort to get them. Explanations should be provided automatically if this can be done relatively unobtrusively, or by hypertext links, and should be context-specific rather than generic. Explanations that conform to Toulmin's model of argumentation, in that they provide adequate justification for the knowledge offered, should be more persuasive and lead to greater trust, agreement, satisfaction, and acceptance--of the explanation and possibly also of the system as a whole. [ABSTRACT FROM AUTHOR]

Open distributed environments, such as the World Wide Web, facilitate information sharing but provide limited support to the protection of sensitive information and resources. Trust negotiation (TN) frameworks have been proposed as a better solution for open environments, in which parties may get in touch and interact without being previously known to each other. In this paper, we illustrate Protune, a rule-based TN system. By describing Protune, we will illustrate the advantages that arise from an advanced rule-based approach in terms of deployment efforts, user friendliness, communication efficiency, and interoperability. The generality and technological feasibility of Protune's approach are assessed through an extensive analysis and experimental evaluations. [ABSTRACT FROM PUBLISHER]