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Copyright of JMD: Journal für Mathematik-Didaktik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper establishes requirements for assessing the usability of Explainable Artificial Intelligence (XAI) methods, focusing on non-AI experts like healthcare professionals. Through a synthesis of literature and empirical findings, it emphasizes achieving optimal cognitive load, task performance, and task time in XAI explanations. Key components include tailoring explanations to user expertise, integrating domain knowledge, and using non-propositional representations for comprehension. The paper highlights the critical role of relevance, accuracy, and truthfulness in fostering user trust. Practical guidelines are provided for designing transparent and user-friendly XAI explanations, especially in high-stakes contexts like healthcare. Overall, the paper's primary contribution lies in delineating clear requirements for effective XAI explanations, facilitating human-AI collaboration across diverse domains. [ABSTRACT FROM AUTHOR]

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]

People often offer an excuse or an apology after they do something wrong in an attempt to mitigate any potential negative consequences. In this paper, we examine how individuals employ social accounts when explaining their interpersonal transgression of meeting lateness to others in actual work settings. We examined the different combinations of social accounts and the social outcomes (forgiveness, helping behaviors, and intentions to continue interaction) of being late to a meeting. Across two studies using complementary experimental and survey methods, we found that a majority of late arrivers' explanations included remorse and that including remorse significantly influences helping behaviors. Furthermore, we found no interaction between excuses and offering remorse. Implications of these findings and future directions are discussed. [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]

The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environment and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs’ behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have ‘seen’, done, and might do in environments in which they operate. In this paper, we provide a comprehensive survey of the existing work in explainable autonomous driving. First, we open by providing a motivation for explanations by highlighting the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their AV explanation requirements. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, vehicle control, and system management). Finally, we discuss pertinent challenges and provide recommendations including a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explanation provisions in autonomous driving. [ABSTRACT FROM AUTHOR]

When justifying a mathematical statement, textbooks often interweave several instances of justification, creating, what we term, paths of justification. This study aims to characterize the paths of justification of mathematical statements that are offered in eight 7th grade Israeli mathematics textbooks. The study attended to context and compared paths of justification in different mathematical domains (algebra and geometry) and in textbooks intended for students with different achievement levels. Analysis focused on the number of instances of justification in a path, the types of justification of those instances, and their order. Comparing paths of justification between algebra and geometry, and between textbooks intended for students with different levels of achievements, revealed several similarities and differences. The findings portray a complex picture of textbook justifications, underlining the need to be sensitive to context when studying textbook justifications. The study results are discussed in light of relevant literature, highlighting potential factors involved in the way a textbook path of justification of a specific mathematical statement is structured. [ABSTRACT FROM AUTHOR]

Background: Deep learning has demonstrated significant advancements across various domains. However, its implementation in specialized areas, such as medical settings, remains approached with caution. In these high-stake environments, understanding the model's decision-making process is critical. This study assesses the performance of different pretrained Bidirectional Encoder Representations from Transformers (BERT) models and delves into understanding its decision-making within the context of medical image protocol assignment. Methods: Four different pre-trained BERT models (BERT, BioBERT, ClinicalBERT, RoBERTa) were fine-tuned for the medical image protocol classification task. Word importance was measured by attributing the classification output to every word using a gradient-based method. Subsequently, a trained radiologist reviewed the resulting word importance scores to assess the model's decision-making process relative to human reasoning. Results: The BERT model came close to human performance on our test set. The BERT model successfully identified relevant words indicative of the target protocol. Analysis of important words in misclassifications revealed potential systematic errors in the model. Conclusions: The BERT model shows promise in medical image protocol assignment by reaching near human level performance and identifying key words effectively. The detection of systematic errors paves the way for further refinements to enhance its safety and utility in clinical settings. [ABSTRACT FROM AUTHOR]

Copyright of Larq Journal for Philosophy, Linguistics & Social Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

This paper discusses a class of mechanistic explanations employed in engineering science where the activities and organization of nonstandard entities (voids, cracks, pits…) are cited as core factors responsible for failures. Given the use of mechanistic language by engineers and the manifestly mechanistic structure of these explanations, I consider several interpretations of these explanations within the new mechanical framework (among others: voids should be considered as shorthand expressions for other entities, voids should be reduced to lower-level mechanisms, or the explanations are simply abstract mechanistic explanations). I argue that these interpretations fail to solve several philosophical problems and propose an account of fictional mechanism explanations instead. According to this account, fictional mechanism explanations provide descriptions of fictional mechanisms that enable the tracking of counterfactual dependencies of the physical system they model by capturing system constraints. Engineers use these models to learn about and understand properties of materials, to build computational simulations of their behaviour, and to design new materials. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has posed numerous legal--ethical challenges. These challenges are particularly acute when dealing with AI demonstrating substantial computational prowess, which is then correlated with agency or autonomy. A common response to considering this issue is to inquire whether an AI system is "conscious" or not. If it is, then it could constitute an agent, actor, or person. This framing is, however, unhelpful since there are many unresolved questions about consciousness. Instead, a practical approach is proposed, which could be used to better regulate new AI technologies. The value of the practical approach in this study is that it (1) provides an empirically observable, testable framework that contains predictive value; (2) is derived from a data-science framework that uses semantic information as amarker; (3) relies on a self-referential logic which is fundamental to agency; (4) enables the "grading" or "ranking" of AI systems, which provides an alternative method (as opposed to current risk-tiering approaches) and measure to determine the suitability of an AI system within a specific domain (e.g., such as social domains or emotional domains); (5) presents consistent, coherent, and higher informational content as opposed to other approaches; (6) fits within the conception of what informational content "laws" are to contain and maintain; and (7) presents a viable methodology to obtain "agency", "agent", and "personhood", which is robust to current and future developments in AI technologies and society. [ABSTRACT FROM AUTHOR]

Few empirical studies have examined how people understand counterfactual explanations for other people's decisions, for example, "if you had asked for a lower amount, your loan application would have been approved". Yet many current Artificial Intelligence (AI) decision support systems rely on counterfactual explanations to improve human understanding and trust. We compared counterfactual explanations to causal ones, i.e., "because you asked for a high amount, your loan application was not approved", for an AI's decisions in a familiar domain (alcohol and driving) and an unfamiliar one (chemical safety) in four experiments (n = 731). Participants were shown inputs to an AI system, its decisions, and an explanation for each decision; they attempted to predict the AI's decisions, or to make their own decisions. Participants judged counterfactual explanations more helpful than causal ones, but counterfactuals did not improve the accuracy of their predictions of the AI's decisions more than causals (Experiment 1). However, counterfactuals improved the accuracy of participants' own decisions more than causals (Experiment 2). When the AI's decisions were correct (Experiments 1 and 2), participants considered explanations more helpful and made more accurate judgements in the familiar domain than in the unfamiliar one; but when the AI's decisions were incorrect, they considered explanations less helpful and made fewer accurate judgements in the familiar domain than the unfamiliar one, whether they predicted the AI's decisions (Experiment 3a) or made their own decisions (Experiment 3b). The results corroborate the proposal that counterfactuals provide richer information than causals, because their mental representation includes more possibilities. [ABSTRACT FROM AUTHOR]

Reciprocal Recommender Systems (RRS) aim to recommend relevant matches to users based on the mutual agreement of their preferences. Explainability of reciprocal recommendations is important for developing a persuasive reciprocal recommender system, since it can improve the effectiveness and credibility of the reciprocal recommendation results. Explainable RRS provide an explanation highlighting why a recommendation would be relevant to the user. Explaining the rationale behind predictions with textual or visual artifacts help in increasing trustworthiness and transparency of the system which is crucial especially for models that are used in critical decision making. In this work, XSiameseBiGRU-UCB, a deep learning contextual bandits framework with post-hoc argumentation based explanations for RRS is proposed. XSiameseBiGRU-UCB is an explainable Siamese neural network-based framework that provides explanations to justify the generated reciprocal recommendations for both the parties involved. In RRS, dilemma between exploitation and exploration requires identifying the best possible recommendation from known information or collecting more information about the environment while generating reciprocal recommendations. To tackle this, we propose to use a contextual bandit policy with upper confidence bound, which adaptively exploits and explores user interests to achieve increased rewards in the long run. Experimental studies conducted with four real-world datasets validate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Deep networks have shown strong performance in glioma grading; however, interpreting their decisions remains challenging due to glioma heterogeneity. To address these challenges, the proposed solution is the Causal Segmentation Framework (CSF). This framework aims to accurately predict high- and low-grade gliomas while simultaneously highlighting key subregions. Our framework utilizes a shrinkage segmentation method to identify subregions containing essential decision information. Moreover, we introduce a glioma grading module that combines deep learning and traditional approaches for precise grading. Our proposed model achieves the best performance among all models, with an AUC of 96.14%, an F1 score of 93.74%, an accuracy of 91.04%, a sensitivity of 91.83%, and a specificity of 88.88%. Additionally, our model exhibits efficient resource utilization, completing predictions within 2.31s and occupying only 0.12 GB of memory during the test phase. Furthermore, our approach provides clear and specific visualizations of key subregions, surpassing other methods in terms of interpretability. In conclusion, the Causal Segmentation Framework (CSF) demonstrates its effectiveness at accurately predicting glioma grades and identifying key subregions. The inclusion of causality in the CSF model enhances the reliability and accuracy of preoperative decision-making for gliomas. The interpretable results provided by the CSF model can assist clinicians in their assessment and treatment planning. [ABSTRACT FROM AUTHOR]

In this paper we argue that transparency of machine learning algorithms, just as explanation, can be defined at different levels of abstraction. We criticize recent attempts to identify the explanation of black box algorithms with making their decisions (post-hoc) interpretable, focusing our discussion on counterfactual explanations. These approaches to explanation simplify the real nature of the black boxes and risk misleading the public about the normative features of a model. We propose a new form of algorithmic transparency, that consists in explaining algorithms as an intentional product, that serves a particular goal, or multiple goals (Daniel Dennet's design stance) in a given domain of applicability, and that provides a measure of the extent to which such a goal is achieved, and evidence about the way that measure has been reached. We call such idea of algorithmic transparency "design publicity." We argue that design publicity can be more easily linked with the justification of the use and of the design of the algorithm, and of each individual decision following from it. In comparison to post-hoc explanations of individual algorithmic decisions, design publicity meets a different demand (the demand for impersonal justification) of the explainee. Finally, we argue that when models that pursue justifiable goals (which may include fairness as avoidance of bias towards specific groups) to a justifiable degree are used consistently, the resulting decisions are all justified even if some of them are (unavoidably) based on incorrect predictions. For this argument, we rely on John Rawls's idea of procedural justice applied to algorithms conceived as institutions. [ABSTRACT FROM AUTHOR]

Copyright of Biology Teaching is the property of East China Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Companies frequently offer product recommendations to customers, according to various algorithms. This research explores how companies should frame the methods they use to derive their recommendations, in an attempt to maximize click-through rates. Two common framings—user-based and item-based—might describe the same recommendation. User-based framing emphasizes the similarity between customers (e.g., "People who like this also like..."); item-based framing instead emphasizes similarities between products (e.g., "Similar to this item"). Six experiments, including two field experiments within a mobile app, show that framing the same recommendation as user-based (vs. item-based) can increase recommendation click-through rates. The findings suggest that user-based (vs. item-based) framing informs customers that the recommendation is based on not just product matching but also taste matching with other customers. Three theoretically derived and practically relevant boundary conditions related to the recommendation recipient, the products, and other users also offer practical guidance for managers regarding how to leverage recommendation framings to increase recommendation click-throughs. [ABSTRACT FROM AUTHOR]

Income inequality is growing in many parts of the world and, for the poorest children in a society, is associated with multiple, negative, developmental outcomes. This review of the research literature considers how childrens' and adolescents' understanding of economic inequality changes with age. It highlights shifts in conceptual understanding (from 'having and not having', to social structural and moral explanations), moral reasoning and the impact of the agents of socialization from parents to the media and cultural norms and discourses. It also examines how social processes affect judgements and the importance of an emerging sense of self in relation to questions of economic inequality. Finally, the review covers methodological considerations and suggests pathways for future research. [ABSTRACT FROM AUTHOR]

For critical operational decisions (e.g. consulting services), explanations and interpretable results of powerful Artificial Intelligence (AI) systems are becoming increasingly important. Knowledge graphs possess a semantic model that integrates heterogeneous information sources and represents knowledge elements in a machine-readable form. The integration of knowledge graphs and machine learning methods represents a new form of hybrid intelligent systems that benefit from each other's strengths. Our research aims at an explainable system with a specific knowledge graph architecture that generates human-understandable results even when no suitable domain experts are available. Against this background, the interpretability of a knowledge graph-based explainable AI approach for business process analysis is focused. We design a framework of interpretation, show how interpretable models are generated by a single case study and evaluate the applicability of our approach by different expert interviews. Result paths on weaknesses and improvement measures related to a business process are used to produce stochastic decision trees, which improve the interpretability of results. This can lead to interesting consulting self-services for clients or be applied as a device for accelerating classical consulting projects. [ABSTRACT FROM AUTHOR]

This article introduces a network approach to describe the quality of written scientific explanations. Existing approaches evaluate explanations mainly on the level of sentences or as a whole but not on the elementary level of single terms. Moreover, evaluation of explanations is often based on highly inferential scoring techniques. We addressed both issues by converting the elementary structure of terms in explanations into networks (so-called element maps) and analysing these with mathematical measures, thus extracting the size and complexity of an explanation, adequacy, coherence, and use of key terms. A total of 65 explanations of experts and students were analysed quantitatively and qualitatively. Differences between expert and student maps' measures can be interpreted meaningfully against the background of existing research findings. Thus, we argue that our approach using network analysis provides a precise, fine-grained, and low-inferential tool that complements and refines existing approaches. Element maps have the potential to improve teaching and research by precisely revealing the strengths and weaknesses of explanations. [ABSTRACT FROM AUTHOR]

We propose answer-set programs that specify and compute counterfactual interventions on entities that are input on a classification model. In relation to the outcome of the model, the resulting counterfactual entities serve as a basis for the definition and computation of causality-based explanation scores for the feature values in the entity under classification, namely responsibility scores. The approach and the programs can be applied with black-box models, and also with models that can be specified as logic programs, such as rule-based classifiers. The main focus of this study is on the specification and computation of best counterfactual entities, that is, those that lead to maximum responsibility scores. From them one can read off the explanations as maximum responsibility feature values in the original entity. We also extend the programs to bring into the picture semantic or domain knowledge. We show how the approach could be extended by means of probabilistic methods, and how the underlying probability distributions could be modified through the use of constraints. Several examples of programs written in the syntax of the DLV ASP-solver, and run with it, are shown. [ABSTRACT FROM AUTHOR]

Learning from instructional explanations is one of the most established, prevalent, and obvious ways of learning--but it carries the risk of shallow processing. Unlike previous research that focused on providing digital just-in-time support measures for learning with explanations, we strived to prepare learners on how to make the most of upcoming explanations. We thus developed a short-term computer-based training intervention on the focused processing of instructional explanations. In two experiments (N1 = 47, N2 = 42), we tested its effects on learning processes and outcomes of a subsequent learning phase. Our results revealed that the training intervention fostered domain-general knowledge about explanations. Furthermore, it enabled learners to benefit from future instructional explanations in other domains (inter-domain transfer for university students, Experiment 1) or at least on other topics (intra-domain transfer for primary school fourth graders, Experiment 2). The digital training intervention did not trigger more cognitive load in the subsequent learning phase. All in all, we describe an initial promising step toward a generic training effect that has the potential advantage of enhancing learning from explanations without altering the actual learning material. [ABSTRACT FROM AUTHOR]

Conviction Narrative Theory (CNT) is a theory of choice under radical uncertainty – situations where outcomes cannot be enumerated and probabilities cannot be assigned. Whereas most theories of choice assume that people rely on (potentially biased) probabilistic judgments, such theories cannot account for adaptive decision-making when probabilities cannot be assigned. CNT proposes that people use narratives – structured representations of causal, temporal, analogical, and valence relationships – rather than probabilities, as the currency of thought that unifies our sense-making and decision-making faculties. According to CNT, narratives arise from the interplay between individual cognition and the social environment, with reasoners adopting a narrative that feels "right" to explain the available data; using that narrative to imagine plausible futures; and affectively evaluating those imagined futures to make a choice. Evidence from many areas of the cognitive, behavioral, and social sciences supports this basic model, including lab experiments, interview studies, and econometric analyses. We identify 12 propositions to explain how the mental representations (narratives) interact with four inter-related processes (explanation, simulation, affective evaluation, and communication), examining the theoretical and empirical basis for each. We conclude by discussing how CNT can provide a common vocabulary for researchers studying everyday choices across areas of the decision sciences. [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]

The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders, including the system engineers, the system's operators and the individuals whose case is being decided. While a variety of interpretability and explainability methods is available, none of them is a panacea that can satisfy all diverse expectations and competing objectives that might be required by the parties involved. We address this challenge in this paper by discussing the promises of Interactive Machine Learning for improved transparency of black-box systems using the example of contrastive explanations—a state-of-the-art approach to Interpretable Machine Learning. Specifically, we show how to personalise counterfactual explanations by interactively adjusting their conditional statements and extract additional explanations by asking follow-up "What if?" questions. Our experience in building, deploying and presenting this type of system allowed us to list desired properties as well as potential limitations, which can be used to guide the development of interactive explainers. While customising the medium of interaction, i.e., the user interface comprising of various communication channels, may give an impression of personalisation, we argue that adjusting the explanation itself and its content is more important. To this end, properties such as breadth, scope, context, purpose and target of the explanation have to be considered, in addition to explicitly informing the explainee about its limitations and caveats. Furthermore, we discuss the challenges of mirroring the explainee's mental model, which is the main building block of intelligible human–machine interactions. We also deliberate on the risks of allowing the explainee to freely manipulate the explanations and thereby extracting information about the underlying predictive model, which might be leveraged by malicious actors to steal or game the model. Finally, building an end-to-end interactive explainability system is a challenging engineering task; unless the main goal is its deployment, we recommend "Wizard of Oz" studies as a proxy for testing and evaluating standalone interactive explainability algorithms. [ABSTRACT FROM AUTHOR]

The massive presence of online learning resources leads many students to have more information than they can consume efficiently. Therefore, students do not always find adaptive learning material for their needs and preferences. In this paper, we present a Conversational Educational Recommender System (C-ERS), which helps students in the process of finding the more appropriated learning resources considering their learning objectives and profile. The recommendation process is based on an argumentation-based approach that selects the learning objects that allow a greater number of arguments to be generated to justify their suitability. Our system includes a simple and intuitive communication interface with the user that provides an explanation to any recommendation. This allows the user to interact with the system and accept or reject the recommendations, providing reasons for such behavior. In this way, the user is able to inspect the system's operation and understand the recommendations, while the system is able to elicit the actual preferences of the user. The system has been tested online with a real group of undergraduate students in the Universidad Nacional de Colombia, showing promising results. [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]

This paper makes two contributions towards enabling a robot to provide explanatory descriptions of its decisions, the underlying knowledge and beliefs, and the experiences that informed these beliefs. First, we present a theory of explanations comprising (i) claims about representing, reasoning with, and learning domain knowledge to support the construction of explanations; (ii) three fundamental axes to characterize explanations; and (iii) a methodology for constructing these explanations. Second, we describe an architecture for robots that implements this theory and supports scalability to complex domains and explanations. We demonstrate the architecture's capabilities in the context of a simulated robot (a) moving target objects to desired locations or people; or (b) following recipes to bake biscuits. [ABSTRACT FROM AUTHOR]

This study explored whether different types of instructional visuals—knowledge maps and pictorial illustrations—encourage students to focus on specific types of conceptual relationships during learning. Undergraduates (n = 134) studied a text lesson on the human nervous system accompanied by maps (text-with-maps group), illustrations (text-with-illustrations group), or no visuals (text-only group). Then all students orally explained what they learned as if they were teaching a peer. The text-with-maps group generated more hierarchical relationships than the other two groups, and both visual groups generated more temporal relationships than the text-only group. The groups did not significantly differ in the number of structural relationships generated. On a subsequent post-test, only the text-with-maps group significantly outperformed the text-only group, and the two visual groups did not significantly differ from each other. These findings highlight how different visuals affect the types of relationships students focus on when learning from the same text. [ABSTRACT FROM AUTHOR]

Traditional lectures are commonly understood to be a teacher-centered mode of instruction where the main aim is a provision of explanations by an educator to the students. Recent literature in higher education overwhelmingly depicts this mode of instruction as inferior compared to the desired student-centered models based on active learning techniques. First, using a four-quadrant model of educational environments, we address common confusion related to a conflation of two prevalent dichotomies by focusing on two key dimensions: (1) the extent to which students are prompted to engage actively and (2) the extent to which expert explanations are provided. Second, using a case study, we describe an evolution of tertiary mathematics education, showing how traditional instruction can still play a valuable role, provided it is suitably embedded in a student-centered course design. We support our argument by analyzing the teaching practice and learning environment in a third-year abstract algebra course through the lens of Stanislas Dehaene's theoretical framework for effective teaching and learning. The framework, comprising "four pillars of learning", is based on a state-of-the-art conception of how learning can be facilitated according to cognitive science, educational psychology and neuroscience findings. In the case study, we illustrate how, over time, the unit design and the teaching approach have evolved into a learning environment that aligns with the four pillars of learning. We conclude that traditional lectures can and do evolve to optimize learning environments and that the erection of the dichotomy "traditional instruction versus active learning" is no longer relevant. [ABSTRACT FROM AUTHOR]

Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of the population. The approach allows for exposures acting alone and in synergy with others. The road map of CoOL involves (i) a pre-computational phase used to define a causal model; (ii) a computational phase with three steps, namely (a) fitting a non-negative model on an additive scale, (b) decomposing risk contributions and (c) clustering individuals based on the risk contributions into subgroups; and (iii) a post-computational phase on hypothesis development, validation and triangulation using new data before eventually updating the causal model. The computational phase uses a tailored neural network for the non-negative model on an additive scale and layer-wise relevance propagation for the risk decomposition through this model. We demonstrate the approach on simulated and real-life data using the R package 'CoOL'. The presentation focuses on binary exposures and outcomes but can also be extended to other measurement types. This approach encourages and enables researchers to identify combinations of exposures as potential causes of the health outcome of interest. Expanding our ability to discover complex causes could eventually result in more effective, targeted and informed interventions prioritized for their public health impact. [ABSTRACT FROM AUTHOR]

Objectives: To explore the heterogeneous disability trajectories and construct explainable machine learning models for effective prediction of long-term disability trajectories and understanding the mechanisms of predictions among the elderly Chinese at community level.Methods: This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018. A total of 4149 subjects aged 65 + in 2002 with completed activities of daily living (ADL) information for at least three waves were included. The mixed growth model was used to identify disability trajectories, and five machine learning models were further established to predict disability trajectories using epidemiological variables. An explainable approach was deployed to understand the model's decisions.Results: Three distinct disability trajectories, including normal class (77.3%), progressive class (15.5%), and high-onset class (7.2%), were identified for three-class prediction. The latter two were further merged into abnormal class, accompanied by normal class for two-class prediction. Machine learning, especially random forest and extreme gradient boosting achieved good performance in both two tasks. ADL, age, leisure activity, cognitive function, and blood pressure were key predictors.Conclusion: The findings suggest that machine learning showed good performance and maybe of additional value in analyzing quality indicators in predicting disability trajectories, thereby providing basis to personalize intervention measures. [ABSTRACT FROM AUTHOR]

The reuse of Learning Designs can bring significant advantages to the educational community such as the diffusion of best teaching practices and the improvement of teaching quality and learning outcomes. Although various tools, including Recommender Systems, have been developed to implement the notion of reusing Learning Designs, their adoption by teachers falls short of expectations. This paper investigates the results of providing explanations for Learning Design recommendations to teachers. To this end, we designed and implemented an explanatory mechanism incorporated into a Recommender System, which propose pre-existing Learning Designs to teachers. We then conducted a user-centric evaluation experiment. Overall, this study provides evidence that explanations should be incorporated into Recommender Systems that propose Learning Designs, as a way of improving the teacher-perceived experience and promoting their wider adoption by teachers. The more teachers accept and adopt Recommender Systems that propose Learning Designs, the more the educational community gains the benefits of reusing Learning Designs. [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]

We extend the extant research on neutral recommendation agents (RAs) to those that lack recommendation neutrality and are biased toward sponsors. We first investigate the effects of recommendation neutrality on users' trust and distrust in RAs by comparing a biased RA with sponsorship disclosure with a neutral RA. We then apply a contingency approach to examine the effects of sponsorship disclosure on users' trust and distrust in biased RAs, with explanations for organic recommendations as a contingent factor. A laboratory experiment was conducted in the United States. We determine that users' trust in the biased RA with sponsorship disclosure is lower and that their distrust is higher than that in the neutral RA. Results also show that user trust in a biased RA increases only when explanations for organic recommendations and sponsorship disclosure are both provided. Users' perceived psychological contract violations of an RA have been verified as a key mediator of the examined effects. However, explanations for organic recommendations, sponsorship disclosure, or their combination fail to significantly lower users' distrust in a biased RA. A second experiment conducted in Hong Kong confirms the major findings of the experiment conducted in the United States. Theoretical contributions and practical implications for e-commerce RAs are discussed. This paper was accepted by Anandhi Bharadwaj, information systems. [ABSTRACT FROM AUTHOR]

The introduction of machine learning to small molecule research– an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate - has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or bioactivity have been published, and these are used on a daily basis by chemists to make decisions and prioritize ideas. The emerging field of explainable artificial intelligence is opening up new possibilities for understanding the reasoning that underlies a model. In small molecule research, this means relating contributions of substructures of compounds to their predicted properties, which in turn also allows the areas of the compounds that have the greatest influence on the outcome to be identified. However, there is no interactive visualization tool that facilitates such interdisciplinary collaborations towards interpretability of machine learning models for small molecules. To fill this gap, we present CIME (ChemInformatics Model Explorer), an interactive web-based system that allows users to inspect chemical data sets, visualize model explanations, compare interpretability techniques, and explore subgroups of compounds. The tool is model-agnostic and can be run on a server or a workstation. [ABSTRACT FROM AUTHOR]

The introduction of machine learning to small molecule research– an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate - has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or bioactivity have been published, and these are used on a daily basis by chemists to make decisions and prioritize ideas. The emerging field of explainable artificial intelligence is opening up new possibilities for understanding the reasoning that underlies a model. In small molecule research, this means relating contributions of substructures of compounds to their predicted properties, which in turn also allows the areas of the compounds that have the greatest influence on the outcome to be identified. However, there is no interactive visualization tool that facilitates such interdisciplinary collaborations towards interpretability of machine learning models for small molecules. To fill this gap, we present CIME (ChemInformatics Model Explorer), an interactive web-based system that allows users to inspect chemical data sets, visualize model explanations, compare interpretability techniques, and explore subgroups of compounds. The tool is model-agnostic and can be run on a server or a workstation. [ABSTRACT FROM AUTHOR]

Due to the prominent role of recommender systems in our daily lives, it is increasingly important to inform users why certain items are recommended and personalize these explanations to the user. In this study, we explored how explanations in a music recommender system should be designed to fit the preference of different personal characteristics. More specifically, we investigated three personal characteristics that influence the perception of explanations in music recommender system interfaces: need for cognition, musical sophistication, and openness. For each of these personal characteristics, we designed explanations for users with lower and higher levels of the personal characteristic. Afterward, we conducted for each personal characteristic a within-subject user study in which we compared the two explanations. Based on the results of these user studies, we provide design suggestions to adapt explanations to different levels of these three personal characteristics. In general, we suggest providing explanations up-front for all recommendations at once. For users low in need for cognition, displaying these explanations must be optional. To support users with low musical sophistication, we suggest providing brief explanations that do not require domain knowledge. For users with low openness, we suggest providing explanations with a lower number of explanation elements. [ABSTRACT FROM AUTHOR]

The assumption of social responsibility for all citizens is a hallmark of the welfare state, reflecting a notion of universalism. But in developing countries, old-age pensions only cover small sections of the population, mainly through contributory social insurance. However, over the last 25 years, old-age pension coverage across the global South has expanded unexpectedly, mostly accounted for by the adoption and reform of non-contributory, or 'social', pensions. In this way, developing countries have come closer to universal coverage, but not through social insurance as in Western and Northern Europe. The rise of social pensions was an unlikely event, since well into the 1990s the idea of 'just giving money to the poor' had been rejected by all major actors in the development community. The standard explanatory model of Northern welfare state research would not have predicted the rise either. Using event history analysis (1967–2011), we find that key variables from the Northern standard explanatory model also operate in development contexts, but that three drivers peculiar to developing countries have to be added. The paper draws on a new data base, constructed by the authors, which covers all social pensions in all low- and middle-income countries. [ABSTRACT FROM AUTHOR]

When seeking to explain social regularities (such as gender differences in the labor market) people often rely on internal features of the targets, frequently neglecting structural and systemic factors external to the targets. For example, people might think women leave the job market after childbirth because they are less competent or are better suited for child‐rearing than men, thereby eliding socio‐cultural and economic factors that disadvantage women. Across two studies (total N = 192) we probe 4‐ and 5‐year‐olds and 7‐ and 8‐year‐olds' internal versus structural reasoning about gender. We explore the evaluative and behavioral implications of this reasoning process with both novel gendered behaviors that were experimentally created and familiar gendered behaviors that exist outside of a lab context. We show that children generate more structural explanations, evaluate the structural explanation more positively, expect behaviors to be more mutable, and evaluate gender non‐conforming behaviors more positively when structural cues are provided. However, we also show that such information may be of limited effectiveness at reducing pre‐existing group‐based discriminatory behaviors: children continue to report less willingness to affiliate with peers who display non‐conforming behaviors even in the presence of structural cues. Taken together, these results provide evidence concerning children's structural reasoning about gender categories and shed new light on how such reasoning might affect social evaluations and behavioral intentions. [ABSTRACT FROM AUTHOR]

Purpose – In organizations, individual user’s compliance with business processes is important from a regulatory and efficiency point of view. The restriction of users’ choices by implementing a restrictive information system is a typical approach in many organizations. However, restrictions and mandated compliance may affect employees’ performance negatively. Especially when users need a certain degree of flexibility in completing their work activity. The purpose of this paper is to introduce the concept of directive explanations (DEs). DEs provide context-dependent feedback to users, but do not force users to comply. Design/methodology/approach – The experimental study used in this paper aims at investigating how DEs influence users’ process compliance. The authors used a laboratory experiment to test the proposed hypotheses. Every participant underwent four trials for which business process compliance was measured. Two trial blocks were used to cluster the four trials. Diagrammatic DEs were provided in one of the trial blocks, while textual DEs were provided in the other. Trial blocks were counterbalanced. Findings – The results of the experiment show that DEs influence a user’s compliance, but the effect varies for different types of DEs. The authors believe this study is significant as it empirically examines design characteristics of explanations from knowledge-based systems in the context of business processes. Research limitations/implications – This study is certainly not without limitations. The sample used for this study was drawn from undergraduate information systems management students. The sample is thus not representative of the general population of organizations’ IT users. However, a student sample adequately represents novice IT users, who are not very familiar with a business process. They are particularly suitable to study how users react to first-time contact with a DE. Practical implications – The findings of this study are important to designers and implementers of systems that guide users to follow business processes. As the authors have illustrated with a real-world scenario, an ERP system’s explanation can lack details on how a user can resolve a blocked activity. In situations in which users bypass restricted systems, DEs can guide them to comply with a business process. Particularly diagrammatic explanations, which depict actors, activities, and constraints for a business process, have been found to increase the probability that users’ behavior is business process compliant. Less time may be needed to resolve a situation, which can result in very efficient user-system cooperation. Originality/value – This study makes several important contributions to research on explanations, which are provided by knowledge-based systems. First, the authors conceptualized, designed, and investigated a novel type of explanations, namely, DEs. The results of this study show how dramatic the difference in process compliance performance is when exposed to certain types of DEs (in one group from 57 percent on the initial trial to 82 percent on the fourth trial). This insight is important to derive design guidelines for DE, particularly when multimedia material is used. [ABSTRACT FROM AUTHOR]

Attention maps, a popular heatmap-based explanation method for Visual Question Answering, are supposed to help users understand the model by highlighting portions of the image/question used by the model to infer answers. However, we see that users are often misled by current attention map visualizations that point to relevant regions despite the model producing an incorrect answer. Hence, we propose Error Maps that clarify the error by highlighting image regions where the model is prone to err. Error maps can indicate when a correctly attended region may be processed incorrectly leading to an incorrect answer, and hence, improve users' understanding of those cases. To evaluate our new explanations, we further introduce a metric that simulates users' interpretation of explanations to evaluate their potential helpfulness to understand model correctness. We finally conduct user studies to see that our new explanations help users understand model correctness better than baselines by an expected 30% and that our proxy helpfulness metrics correlate strongly (1>0:97) with how well users can predict model correctness. [ABSTRACT FROM AUTHOR]

Generative learning theory posits that learners engage more deeply and produce better learning outcomes when they engage in selecting, organizing, and integrating processes during learning. The present experiments examine whether the generative learning activity of generating explanations can be extended to online multimedia lessons and whether prompts to engage in this generative learning activity work better than more passive instruction. Across three experiments, college students learned about greenhouse gasses from a 4-part online lesson involving captioned animations and subsequently took a posttest. After each part, learners were asked to generate an explanation (write-an-explanation), write an explanation using provided terms (write-a-focused-explanation), rewrite a provided explanation (rewrite-an-explanation), read a provided explanation (read-an-explanation), or simply move on to the next part (no-activity). Overall, students in the write-an-explanation group (Experiments 2 and 3), write-a-focused-explanation group (Experiment 2), and rewrite-an-explanation group (Experiment 3) performed significantly better on a delayed posttest than the no-activity group, but the groups did not differ significantly on an immediate posttest (Experiment 1). These results are consistent with generative learning theory and help identify generative learning strategies that improve online multimedia learning, thereby priming active learning with passive media. [ABSTRACT FROM AUTHOR]

Conflict detection is used in various scenarios ranging from interactive decision making (e.g., knowledge-based configuration) to the diagnosis of potentially faulty models (e.g., using knowledge base analysis operations). Conflicts can be regarded as sets of restrictions (constraints) causing an inconsistency. Junker's QuickXPlain is a divide-and-conquer based algorithm for the detection of preferred minimal conflicts. In this article, we present a novel approach to the detection of such conflicts which is based on speculative programming. We introduce a parallelization of QuickXPlain and empirically evaluate this approach on the basis of synthesized knowledge bases representing feature models. The results of this evaluation show significant performance improvements in the parallelized QuickXPlain version. [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]