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Philosophical theorizing on democracy typically assumes away the existence of nonhuman animals – even though animals are systematically affected by democratic decisions in morally relevant ways. This paper inspects under which circumstances, if at all, this omission can be justified. First, I distinguish between two possible explanations of this neglect: (i) that animals are neglected by democratic theorists due to speciesist biases, according to a debunking account, or (ii) that animals are deliberately idealized away, on a reconstructive approach. Largely, this paper is devoted to assessing when assuming away animals can function as a theoretically productive or practically helpful idealization when theorizing about democracy. In particular, I identify three different aims that may be served by idealizing animals away in democratic theory: (i) to understand what makes democracy valuable, legitimate, or authoritative, (ii) to assess how the pitfalls of really existing democracies might be fixed and (iii) to isolate what robust agents owe each other in democratic societies. I then argue that, whereas the third goal might be usefully advanced by idealizing animals away, the first two goals can only be suitably tackled if we take into account how democracies affect animals’ interests. [ABSTRACT FROM AUTHOR]

In a seminal paper, Kiparsky & Kiparsky (1970) proposed a two‐way correspondence between presuppositionality of clauses and nominal structure. The proposal remains highly relevant to current research (a.o. Kastner 2015, Bochnak & Hanink 2022), despite the existence of counterexamples in both directions. In this paper, we examine Russian nominalized clauses to show that presuppositionality is indeed neither necessary nor sufficient for nominalization. However, instead of completely discarding the correspondence between presuppositionality and nominalization, we argue for a weaker "preference" hypothesis, whereby presuppositional, or discourse‐given, contexts are associated with a higher likelihood of nominalization compared to discourse‐new contexts. We provide support for the preference hypothesis based on four experimental studies, a forced‐choice and a givenness‐rating study using matrix negation as a proxy for givenness and a forced‐choice and a sentence completion study directly manipulating the discourse context. We suggest a tentative explanation for the preference hypothesis in terms of definiteness/familiarity marking. [ABSTRACT FROM AUTHOR]

This paper responds to Friend's (2023) critique of the Powers-BSA , a view according to which laws of nature are efficient descriptions of how modally laden properties (powers) are possibly distributed in spacetime. In the course of this response, the paper discusses the nature of scientific and metaphysical explanation, the aim of science, and the structure of modal space. [ABSTRACT FROM AUTHOR]

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance. [ABSTRACT FROM AUTHOR]

This paper introduces a special issue focusing on the intricacies of Turkey's party system and the evolution of Turkish party competition amid democratic challenges. The introduction provides a brief overview of each paper in this special issue, which delves into the relationship between economic factors and voter behavior, offering insights into the continued dominance of the ruling party. While doing so, the special issue specifically examines the 2023 Turkish General elections, presenting alternative perspectives on how the incumbent party maintains its electoral success. [ABSTRACT FROM AUTHOR]

Scientific skepticism about attention has run deeply for a century. We are said to not know what attention is despite over 45 000 scientific papers published on attention since 1950. The theory of attention is shown to be logically inconsistent. Since too many things are said to be attention, the theory faces a dilemma: contradiction or confusion. Empirical methodology provides a solution, entailing a common conception of attention: attention is a subject mentally selecting a target to guide response. The common conception provides a methodological norm yielding a functional structure built into every attentional paradigm, so something we all accept and thereby know. The common conception unifies empirical work into an explanatory theory. Attention is typically what we are trying to explain (effect) and is not an explainer (cause). Attention is one of the most thoroughly investigated psychological phenomena, yet skepticism about attention is widespread: we do not know what it is, it is too many things, there is no such thing. The deficiencies highlighted are not about experimental work but the adequacy of the scientific theory of attention. Combining common scientific claims about attention into a single theory leads to internal inconsistency. This paper demonstrates that a specific functional conception of attention is incorporated into the tasks used in standard experimental paradigms. In accepting these paradigms as valid probes of attention, we commit to this common conception. The conception unifies work at multiple levels of analysis into a coherent scientific explanation of attention. Thus, we all know what attention is. [ABSTRACT FROM AUTHOR]

The paper explores Galen's notion of the chreia of bodily parts and activities, and the method of its discovery against the Aristotelian background. It argues that the chreia of an object π (a bodily part or activity) is a connection between the activity ε for the sake of which π has come into existence, and the attributes of π without which ε would cease to exist or would not be as good. The discovery of chreia , then, is an explanation of this connection. Aristotle does not use the word ' chreia ' in this sense, but in Parts of Animals he employs a partly overlapping notion which he calls ' ergon '. Finally, the paper points out that Galen's chreia is equivalent to the middle term of teleological demonstrations, as outlined in Aristotle's Posterior Analytics II 11. [ABSTRACT FROM AUTHOR]

When learning about scientific phenomena, students are expected to mechanistically explain how underlying interactions produce the observable phenomenon and conceptually connect the observed phenomenon to canonical scientific knowledge. This paper investigates how the integration of the complementary processes of designing and refining computational models using real‐world data can support students in developing mechanistic and canonically accurate explanations of diffusion. Specifically, we examine two types of shifts in how students explain diffusion as they create and refine computational models using real‐world data: a shift towards mechanistic reasoning and a shift from noncanonical to canonical explanations. We present descriptive statistics for the whole class as well as three student work examples to illustrate these two shifts as 6th grade students engage in an 8‐day unit on the diffusion of ink in hot and cold water. Our findings show that (1) students develop mechanistic explanations as they build agent‐based models, (2) students' mechanistic reasoning can co‐exist with noncanonical explanations, and (3) students shift their thinking toward canonical explanations after comparing their models against data. These findings could inform the design of modeling tools that support learners in both expressing a diverse range of mechanistic explanations of scientific phenomena and aligning those explanations with canonical science. [ABSTRACT FROM AUTHOR]

The study of teleology is challenging in many ways, but there is a particular challenge that makes matters worse, distorting the conceptual space that has set the terms of debate. And that is the tendency to think about teleology in terms of certain long‐established dichotomies. In this paper, we examine four such dichotomies prevalent in the literature on teleology, the notions that: 1) Teleological explanations are opposed to mechanistic explanations; 2) teleology must arise from processes operating either internal to an organism or external to it; 3) systems are either alive and teleological, or nonliving and not teleological; 4) humans are teleological, on account of our ability to intend, seek, prefer, etc., while other systems without these capacities are not. Here, we use our own view of goal directedness, field theory, to show for each dichotomy that there is an alternative, a view of teleology that either violates these dichotomies or demands revision of them. What this reveals is not only the dangers of dichotomous thinking, but a widespread lack of clarity about what teleology is. [ABSTRACT FROM AUTHOR]

An important focus in discussions of immunity to error through misidentification (IEM) is on what explains putative cases of it, and correspondingly, on what significance its presence should be taken to have. This focus is important for the literature: the interest of the phenomenon naturally depends on why it is supposed to be significant when it arises. This paper looks to the claimed IEM of memory-based judgments as a case study to advocate for a pluralist account of explanations of IEM. I argue that Evans was right that Shoemakerian q-memories fail to undermine the supposed IEM of memory-based first personal judgments, but that he was wrong about why. In fact, cases of q-memory reveal a surprising referential underdetermination in first person thought, which subvenes an underdetermination of the explanation of IEM for memory-based judgments. Given this plurality of explanations active in just a single case study, we should be cautious about claims to IEM having just one source of significance. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (or GANs) are a specific deep learning architecture often used for different usages, such as data generation or image-to-image translation. In recent years, this structure has gained increased popularity and has been used in different fields. One area of expertise currently in vogue is the use of GANs to produce synthetic aperture radar (SAR) data, and especially expand training datasets for SAR automatic target recognition (ATR). In effect, the complex SAR image formation makes these kind of data rich in information, leading to the use of deep networks in deep learning-based methods. Yet, deep networks also require sufficient data for training. However, contrary to optical images, we generally do not have a substantial number of available SAR images because of their acquisition and labelling cost; GANs are then an interesting tool. Concurrently, how to improve explainability for SAR ATR deep neural networks and how to make their reasoning more transparent have been increasingly explored as model opacity deteriorates trust of users. This paper aims at reviewing how GANs are used with SAR images, but also giving perspectives on how GANs could be used to improve interpretability and explainability of SAR classifiers. [ABSTRACT FROM AUTHOR]

This paper argues that the so‐called ‘paradox’ of dehumanization is a mirage arising from misplaced abstraction. The alleged ‘paradox’ is taken as a challenge that arises from a skeptical stance. After reviewing the history of that skeptical stance, it is reconstructed as an argument with two premises. With the help of an epistemologically structured but pluralistic frame it is then shown how the two premises of the Skeptic's argument can both be debunked. As part of that it emerges that there are a couple of ways how dehumanization can be realized, and one such realization can be sufficient for affirming the reality of dehumanization for a specific case. [ABSTRACT FROM AUTHOR]

Machine learning methods have been extensively used in survival analysis. SurvivalBoost - a machine-learning-based survival regression algorithm, focused on Elastic-net-Type penalized semiparametric Cox regression model on XGBoost and random survival forests, has been verified its superior prediction performance on real and simulated datasets. Whereas the interpretability is remain undiscovered. This paper discusses the interpretability of this algorithm upon using the Shapley Additive Explanation (SHAP) value. It is illustrated that the algorithm can be more effective to guide the diagnosis and practice of survival analysis. [ABSTRACT FROM AUTHOR]

This paper evaluates the potentials of Mullā Şadrā's philosophy to explain the possibility of the Islamization of philosophy. This contributes to a more general, yet controversial, project in post-revolutionary Iran, namely the Islamization of knowledge, especially in the humanities. If there would be a mechanism through which Islamization of philosophy – as a historical example and as a field of knowledge that provides theoretical grounds of other humanities – can be plausibly explained, then one might think of Islamizing the humanities, as well. One famous explanation in Iran is based on the distinction between contexts of discovery and justification. It explains how a philosopher is permitted to use the Islamic content in the context of discovery – not justification – and how this helps to Islamize philosophy. In this article, we claim a new explanation which is based on Mullā Şadrā's transcendent philosophy. Such a philosophical explanation not only acknowledges using the Qur'an and authentic narrations in the context of discovery but also necessitates involving revelation as a philosophical criterion in the context of justification and a highly valid epistemic source in producing the new philosophical content. This provides a strong account of the Islamization of philosophy. [ABSTRACT FROM AUTHOR]

Instructional explanations are sometimes viewed as part of a nonconstructivist, solely teacher‐centered learning environment, leading to the perception that they are ineffective or inappropriate for teaching science. Consequently, teacher education programmes seldom focus on preparing teachers to explain scientific concepts effectively. Interestingly, the perception of a specific kind of instructional explanation in teaching has evolved in recent years: explanatory videos, in particular, are being viewed as promising digital tools for learning. This article asserts that instructional explanations constitute integral components within nearly all learning environments where communication about science takes place. It has two goals. Firstly, the article aims to develop a coherent, constructivist theory of explaining, including both teacher explanations and explanatory videos. This theory offers an inductive‐statistical explanation of the underlying mechanisms of communicative situations that involve experts and novices. Secondly, based on this constructivist perspective, the article distinguishes instructional explanations from scientific explanations and argumentation. It contends that (a) reducing instructional explanations solely to teacher‐centered, didactic teaching represents a misconception with potentially adverse effects and (b) it also is a misconception that instructional explanations, scientific explanations, and argumentation are (nearly) interchangeable. The paper argues that instructional explanations, including both teacher explanations and explanatory videos, are not only a potentially effective part of all kinds of science teaching but also a core practice of science teachers. [ABSTRACT FROM AUTHOR]

The paper argues that action explanations of the form ‘because p’ do not indicate that reasons are non-psychological facts or propositions. ‘Because p’ has two different uses: In the explanatory use, ‘because’ operates on the alleged fact that p. In the reason-giving use, however, ‘because’ operates not on p, but on the agent’s belief that p: she does not describe but express her reason. I conclude that a proper analysis of reason-giving ‘because’-utterances suggests that reasons are mental states. [ABSTRACT FROM AUTHOR]

This paper analyzes a particular case of the maps of the Geographical Relations of 1577 belonging to the Chichimeca cultural region, north of the viceroyalty of New Spain. The analysis that has been carried out part of the methodology of reconstruction of the landscape and internal analysis of the document to, in this way, explain in detail the different elements corresponding to Military History in the document, as well as its explanation and contextualization within the conflict that developed between 1550-1600. [ABSTRACT FROM AUTHOR]

Roche and Sober (2013; 2014; 2017; 2019) have offered an important new argument that explanatoriness lacks confirmatory significance. My aim in this paper is not only to contend that their argument fails to show that in confirmation 'there is nothing special about explanatoriness' (Roche & Sober, 2017: 589), but also to reveal what is special confirmationwise about explanatoriness. I will argue that much of the heavy work in Roche and Sober's argument is done by the dichotomy into which they carve up the philosophical options: either explanatory considerations per se are confirmatorily relevant or they acquire whatever confirmatory significance they may possess only by way of background beliefs and so no differently from non‐explanatory considerations. I will argue that this is a false dichotomy: Explanatoriness per se can have confirmatory relevance even while explanatory considerations acquire their confirmatory relevance only through background opinions. Furthermore, there is 'something special' about the background opinions concerning explanations that allow explanatoriness in itself to have confirmatory impact. Unlike other sorts of background, having some background opinions expressly about explanations is indispensable to being an observer at all. [ABSTRACT FROM AUTHOR]

When we learn about a severe moral transgression that has been committed, we are often not only horrified but also puzzled. We are inclined to raise questions such as 'Why did they do this?' or exclaim: 'I cannot understand why anyone would do such a thing!'. This suggests that there is something difficult to understand about severe moral wrongs. In this paper, I offer an explanation of the phenomenon that severe moral transgressions are often difficult to understand. I begin by arguing that the relevant sense in which we try but often fail to understand morally bad action is that we try but fail to empathize with the agent at the time of the action. Then, I introduce a distinction between two broad kinds of morally bad action. Finally, I show that each of these kinds of morally bad action gives rise to obstacles to empathizing with the agent at the time of the action. Based on this explanation, I consider whether we should try to overcome this limitation to our understanding. [ABSTRACT FROM AUTHOR]

This paper investigates conceptions of explanation, teleology, and analogy in the works of Immanuel Kant (1724–1804) and Georges Cuvier (1769–1832). Richards (2000, 2002) and Zammito (2006, 2012, 2018) have argued that Kant's philosophy provided an obstacle for the project of establishing biology as a proper science around 1800. By contrast, Russell (1916), Outram (1986), and Huneman (2006, 2008) have argued, similar to suggestions from Lenoir (1989), that Kant's philosophy influenced the influential naturalist Georges Cuvier. In this article, I wish to expand on and further the work of Russell, Outram, and Huneman by adopting a novel perspective on Cuvier and considering (a) the similar conceptions of proper science and explanation of Kant and Cuvier, and (b) the similar conceptions of the role of teleology and analogy in the works of Kant and Cuvier. The similarities between Kant and Cuvier show, contrary to the interpretation of Richards and Zammito, that some of Kant's philosophical ideas, whether they derived from him or not, were fruitfully applied by some life scientists who wished to transform life sciences into proper sciences around 1800. However, I also show that Cuvier, in contrast to Kant, had a workable strategy for transforming the life sciences into proper sciences, and that he departed from Kant's philosophy of science in crucial respects. • Explains the relevance of Kant's philosophy for the emergence of biology. • Explains Cuvier's conception of proper science and explanation. • Explains Cuvier's regulative conception of teleology and analogy. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence (XAI) evaluation has grown significantly due to its extensive adoption, and the catastrophic consequence of misinterpreting sensitive data, especially in the medical field. However, the multidisciplinary nature of XAI research resulted in diverse scholars possessing significant challenges in designing proper evaluation methods. This paper proposes a novel framework of a three-layered top-down approach on how to arrive at an optimal explainer, accenting the persistent need for consensus in XAI evaluation. This paper also investigates a critical comparative evaluation of explanations in both model agnostic and specific explainers including LIME, SHAP, Anchors, and TabNet, aiming to enhance the adaptability of XAI in a tabular domain. The results demonstrate that TabNet achieved the highest classification recall followed by TabPFN, and XGBoost. Additionally, this paper develops an optimal approach by introducing a novel measure of relative performance loss with emphasis on faithfulness and fidelity of global explanations by quantifying the extent to which a model's capabilities diminish when eliminating topmost features. This addresses a conspicuous gap in the lack of consensus among researchers regarding how global feature importance impacts classification loss, thereby undermining the trust and correctness of such applications. Finally, a practical use case on medical tabular data is provided to concretely illustrate the findings. [ABSTRACT FROM AUTHOR]

This paper holds that Laozi's philosophy on softness is a topic that remains to be fully discussed. By distinguishing between the meanings of softness and weakness, this paper discusses how Wang Bi semantically integrated the two, presenting them as methods to attain the Dao. In this paper, the differences in Wang Bi's usage of "柔弱" (softness and weakness) and "柔顺" (softness and compliance) in his annotations on the Daodejing and Yijing are noted, emphasizing the logical support and rational explanation that Wang Bi provided for the external behaviors of gentleness described in the hexagram lines. Wang Bi reconciled the contradictions between Confucian and Daoist views on valuing gentleness and balancing Yin–Yang. In the text, he elaborates on gentleness as both a personal moral requirement and a method of social governance, addressing the real-world issues of his time and thus greatly enhancing the practicality of Laozi's philosophy of valuing softness. [ABSTRACT FROM AUTHOR]

This paper argues that the project of a lifespan perspective in developmental psychology has not yet been systematically pursued. Overall, the number of age-specific papers far outweighs the number of lifespan approaches, and even approaches that focus on the lifespan as a whole are often restricted to adulthood. Further, there is a lack of approaches that examine cross-lifespan relationships. However, the lifespan perspective has brought with it a "processual turn" that suggests an examination of developmental regulatory processes that are either operative across the lifespan or develop across the lifespan. Accommodative adjustment of goals and evaluations in response to obstacles, loss, and threat is discussed as an example of such a process. Not only is it prototypical of efficacy and change of developmental regulation across the lifespan, but at the same time it makes clear that stability (e.g., of the self)—as a possible outcome of accommodation—is not an alternative to, but a variant of development. Explaining how accommodative adaptation changes, in turn, requires a broader perspective. For this purpose, an evolutionary approach to developmental psychology is proposed that not only views human development as a product of phylogenesis, but also applies the central concepts of the theory of evolution (adaptation and history) directly to ontogeny. The challenges, conditions, and limitations of such a theoretical application of adaptation to human development are discussed. [ABSTRACT FROM AUTHOR]

Background: When research evidence is limited, inconsistent, or absent, healthcare decisions and policies need to be based on consensus among interested stakeholders. In these processes, the knowledge, experience, and expertise of health professionals, researchers, policymakers, and the public are systematically collected and synthesised to reach agreed clinical recommendations and/or priorities. However, despite the influence of consensus exercises, the methods used to achieve agreement are often poorly reported. The ACCORD (ACcurate COnsensus Reporting Document) guideline was developed to help report any consensus methods used in biomedical research, regardless of the health field, techniques used, or application. This explanatory document facilitates the use of the ACCORD checklist. Methods and findings: This paper was built collaboratively based on classic and contemporary literature on consensus methods and publications reporting their use. For each ACCORD checklist item, this explanation and elaboration document unpacks the pieces of information that should be reported and provides a rationale on why it is essential to describe them in detail. Furthermore, this document offers a glossary of terms used in consensus exercises to clarify the meaning of common terms used across consensus methods, to promote uniformity, and to support understanding for consumers who read consensus statements, position statements, or clinical practice guidelines (CPGs). The items are followed by examples of reporting items from the ACCORD guideline, in text, tables, and figures. Conclusions: The ACCORD materials—including the reporting guideline and this explanation and elaboration document—can be used by anyone reporting a consensus exercise used in the context of health research. As a reporting guideline, ACCORD helps researchers to be transparent about the materials, resources (both human and financial), and procedures used in their investigations so readers can judge the trustworthiness and applicability of their results/recommendations. Patricia Logullo and colleagues created an explanatory document that facilitates the use of the ACCORD checklist. [ABSTRACT FROM AUTHOR]

Ranking schemes drive many real-world decisions, like, where to study, whom to hire, what to buy, etc. Many of these decisions often come with high consequences. For example, a university can be deemed less prestigious if not featured in a top-k list, and consumers might not even explore products that do not get recommended to buyers. At the heart of most of these decisions are opaque ranking schemes, which dictate the ordering of data entities, but their internal logic is inaccessible or proprietary. Drawing inferences about the ranking differences is like a guessing game to the stakeholders, like, the rankees (i.e., the entities who are ranked, like product companies) and the decision-makers (i.e., who use the rankings, like buyers). In this paper, we aim to enable transparency in ranking interpretation by using algorithmic rankers that learn from available data and by enabling human reasoning about the learned ranking differences using explainable AI (XAI) methods. To realize this aim, we leverage the exploration–explanation paradigm of human–data interaction to let human stakeholders explore subsets and groupings of complex multi-attribute ranking data using visual explanations of model fit and attribute influence on rankings. We realize this explanation paradigm for transparent ranking interpretation in TRIVEA, a visual analytic system that is fueled by: (i) visualizations of model fit derived from algorithmic rankers that learn the associations between attributes and rankings from available data and (ii) visual explanations derived from XAI methods that help abstract important patterns, like, the relative influence of attributes in different ranking ranges. Using TRIVEA, end users not trained in data science have the agency to transparently reason about the global and local behavior of the rankings without the need to open black-box ranking models and develop confidence in the resulting attribute-based inferences. We demonstrate the efficacy of TRIVEA using multiple usage scenarios and subjective feedback from researchers with diverse domain expertise. [ABSTRACT FROM AUTHOR]

Automated agents, with rapidly increasing capabilities and ease of deployment, will assume more key and decisive roles in our societies. We will encounter and work together with such agents in diverse domains and even in peer roles. To be trusted and for seamless coordination, these agents would be expected and required to explain their decision making, behaviors, and recommendations. We are interested in developing mechanisms that can be used by human-agent teams to maximally leverage relative strengths of human and automated reasoners. We are interested in ad hoc teams in which team members start to collaborate, often to respond to emergencies or short-term opportunities, without significant prior knowledge about each other. In this study, we use virtual ad hoc teams, consisting of a human and an agent, collaborating over a few episodes where each episode requires them to complete a set of tasks chosen from available task types. Team members are initially unaware of the capabilities of their partners for the available task types, and the agent task allocator must adapt the allocation process to maximize team performance. It is important in collaborative teams of humans and agents to establish user confidence and satisfaction, as well as to produce effective team performance. Explanations can increase user trust in agent team members and in team decisions. The focus of this paper is on analyzing how explanations of task allocation decisions can influence both user performance and the human workers' perspective, including factors such as motivation and satisfaction. We evaluate different types of explanation, such as positive, strength-based explanations and negative, weakness-based explanations, to understand (a) how satisfaction and performance are improved when explanations are presented, and (b) how factors such as confidence, understandability, motivation, and explanatory power correlate with satisfaction and performance. We run experiments on the CHATboard platform that allows virtual collaboration over multiple episodes of task assignments, with MTurk workers. We present our analysis of the results and conclusions related to our research hypotheses. [ABSTRACT FROM AUTHOR]

This paper aims to reflect politically and ideologically on the representation of the 2008 crisis and its repercussions on the rural habitat in recent Spanish literary fiction. To this end, it proposes the analysis of five (neo)rural novels, but not before a brief historical-cultural introduction to the post-15M repoliticization process of the novel in Spain, as well as the explanation of the problems surrounding the nomenclatures "rural" and "neo-rural". [ABSTRACT FROM AUTHOR]

To understand why a phenomenon occurs, it is not enough to possess a correct explanation of the phenomenon: you must grasp the explanation. In this formulation, "grasp" is a placeholder, standing for the psychological or epistemic relation that connects a mind to the explanatory facts in such a way as to produce understanding. This paper proposes and defends an account of the "grasping" relation according to which grasp of a property (to take one example of the sort of entity that turns up in explanations) is a matter of recognitional ability: roughly, a property is grasped to the extent to which the would-be understander is capable of recognizing instances of the property. [ABSTRACT FROM AUTHOR]

Scientific understanding is a fundamental goal of science. However, there is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of scientific understanding. In this paper, we propose a framework to create a benchmark for scientific understanding, utilizing tools from philosophy of science. We adopt a behavioral conception of understanding, according to which genuine understanding should be recognized as an ability to perform certain tasks. We extend this notion of scientific understanding by considering a set of questions that gauge different levels of scientific understanding, covering information retrieval, the capability to arrange information to produce an explanation, and the ability to infer how things would be different under different circumstances. We suggest building a Scientific Understanding Benchmark (SUB), formed by a set of these tests, allowing for the evaluation and comparison of scientific understanding. Benchmarking plays a crucial role in establishing trust, ensuring quality control, and providing a basis for performance evaluation. By aligning machine and human scientific understanding we can improve their utility, ultimately advancing scientific understanding and helping to discover new insights within machines. [ABSTRACT FROM AUTHOR]

This paper examines the role of fatalistic beliefs and naïve or spontaneous causal explanations for road accidents with the aim of better understanding road safety behavior in Morocco. We hypothesize that strong fatalistic beliefs will be more related to an explanation of accidents by external factors and to less safe behaviors. The study was conducted with a sample of 1017 Moroccan professional and private drivers, using a questionnaire with scales measuring fatalistic beliefs, naïve causal explanations of road accidents and reported road safety behavior. The results show that drivers with very strong fatalistic beliefs tend to explain road accidents by causes that are external to the drivers and engage in less safe behaviors. We also note that professional drivers tend to be more fatalistic than private drivers and provide more external causal explanations. In addition, we find that when drivers provide internal or dispositional causal explanations, they tend to engage in safer behaviors. The results support the need to consider fatalistic beliefs and naïve causal explanations provided for accidents when designing relevant and targeted prevention messages for motorists. [ABSTRACT FROM AUTHOR]

The thesis of this paper is that, in De Caelo I.2, the introduction and differentiation of simple bodies is achieved entirely by differentiating simple motions. This runs counter to the traditional interpretations of De Caelo I.2, which consider how, and how completely, Aristotle uses the differentiation of simple magnitudes to differentiate simple bodies, and which assume that he introduces the notion of a simple body independently of the notion of a simple motion. But the traditional interpretations miss the point of Aristotle's argument in De Caelo I.2, which – so I argue – is to introduce the notion of simple motions, using this to introduce the notion of simple bodies, and to thereby provide an explanatory account of all possible locomotion. This is the reason why Aristotle identifies simple bodies in De Caelo I.2 with the fundamental components of the universe. [ABSTRACT FROM AUTHOR]

Several philosophers maintain that outright belief exists because it plays a reasoning simplifying role (Holton 2008; Ross and Schroeder 2014; Staffel 2019; Weisberg 2020). This claim has been recently contested, on the grounds that credences also can simplify reasoning (Dinges 2021). This paper takes a step back and asks: what features of an attitude explain its alleged ability to simplify reasoning? The paper contrasts two explanations, one in terms of dispositions and the other in terms of representation, arguing in favour of the latter and against the former. The proposed explanation yields two interesting results: first, both belief and other attitudes, such as acceptance and imagination, can play a reasoning simplifying role; second, credences do not simplify our reasoning. [ABSTRACT FROM AUTHOR]

The use of process-based modelling is increasing in most disciplines of plant science, but criteria for what is acceptable modelling work have rarely been made explicit. Unlike empirical work, modelling does not provide direct information on nature, and the goal is generally to explain natural phenomena, or predict their consequences, rather than reveal new phenomena or test specific hypotheses. Goals, methods and results of modelling and empirical work thus differ and they should be judged by different criteria. This note shows how the four criteria of explanation, testability, novelty and good modelling practice can be applied to modelling papers in the plant sciences. The practical implementation of these criteria is discussed, and a checklist of eight questions is derived with which papers can be evaluated. To illustrate the use of the criteria, they are applied to a classic publication from the crop modelling literature [Simulation of Assimilation, Respiration and Transpiration of Crops. Pudoc, Wageningen]. [Copyright &y& Elsevier]

There are many methods for explaining why a machine learning model produces a given output in response to a given input. The relative merits of these methods are often debated using theoretical arguments and illustrative examples. This paper provides a large-scale empirical test of four widely used explanation methods by comparing how well their algorithmically generated denial reasons align with lender-provided denial reasons using a dataset of home mortgage applications. On a held-out sample of 10,000 denied applications, Shapley additive explanations (SHAP) correspond most closely with lender-provided reasons. SHAP is also the most computationally efficient. As a second contribution, this paper presents a method for computing integrated gradient explanations that can be used for non-differentiable models such as XGBoost. [ABSTRACT FROM AUTHOR]

Knauff and Gazzo Castañeda (2022) object to using the term "new paradigm" to describe recent developments in the psychology of reasoning. This paper concedes that the Kuhnian term "paradigm" may be queried. What cannot is that the work subsumed under this heading is part of a new, progressive movement that spans the brain and cognitive sciences: Bayesian cognitive science. Sampling algorithms and Bayes nets used to explain biases in JDM can implement the Bayesian new paradigm approach belying any advantages of mental models theory (MMT) at the algorithmic level. Moreover, this paper argues that new versions of MMT lack a computational level theory and questions the grounds for MMTs much-vaunted generality. The paper then examines common ground on the importance of small-scale models/simulations of the world and the importance of argumentation in the social domain rather than individual reasoning. Finally, the paper concludes that although there may be prospects for moving reasoning research forward in a more collective, collaborative manner, many disagreements remain to be resolved. [ABSTRACT FROM AUTHOR]

Can atheistic explanations scientifically explain the world and humans? Can theistic deductive arguments provide a rational explanation of the world? Many philosophers of contemporary religion believe that deductive arguments for the existence of God are invalid because they are either deductively invalid or based on one or more false or uncertain premises. Hence, they turned to non-analogical arguments about the existence of God. The purpose of this paper is to examine and evaluate one of these interesting new arguments, the Swinburne inductive argument. This research paper is a library-based theoretical analysis that is done through the concepts, propositions, and arguments. Swinburne claims that humanistic and materialistic explanations cannot explain the world and humans. In contrast, he has three arguments for the existence of the universe, the existence of scientific laws, and the laws of the process of evolution; He uses the existence of God as three affirmative inductive arguments and concludes the accumulation of theism as the best explanation. The result of such arguments is that theism can be the ultimate explanation of the world. It will also be shown that such arguments are less challenging and can greatly increase the likelihood of God's existence. [ABSTRACT FROM AUTHOR]

In this paper, we present Forest GUMP (for Generalized, Unifying Merge Process) a tool for verification and precise explanation of Random forests. Besides pre/post-condition-based verification and equivalence checking, Forest GUMP also supports three concepts of explanation, the well-known model explanation and outcome explanation, as well as class characterization, i.e., the precise characterization of all samples that are equally classified. Key technology to achieve these results is algebraic aggregation, i.e., the transformation of a Random Forest into a semantically equivalent, concise white-box representation in terms of Algebraic Decision Diagrams (ADDs). The paper sketches the method and demonstrates the use of Forest GUMP along illustrative examples. This way readers should acquire an intuition about the tool, and the way how it should be used to increase the understanding not only of the considered dataset, but also of the character of Random Forests and the ADD technology, here enriched to comprise infeasible path elimination. As Forest GUMP is publicly available all experiments can be reproduced, modified, and complemented using any dataset that is available in the ARFF format. [ABSTRACT FROM AUTHOR]

In this paper, we present the envisioned style and scope of the new topic "Explanation Paradigms Leveraging Analytic Intuition" (ExPLAIn) with the International Journal on Software Tools for Technology Transfer (STTT). Intention behind this new topic is to (1) explicitly address all aspects and issues that arise when trying to, if possible, reveal and then confirm hidden properties of black-box systems, or (2) to enforce vital properties by embedding them into appropriate system contexts. Machine-learned systems, such as Deep Neural Networks, are particularly challenging black-box systems, and there is a wealth of formal methods for analysis and verification waiting to be adapted and applied. The selection of papers of this first Special Section of ExPLAIn, most of which were co-authored by editorial board members, is an illustrative example of the style and scope envisioned: In addition to methodological papers on verification, explanation, and their scalability, case studies, tool papers, literature reviews, and position papers are also welcome. [ABSTRACT FROM AUTHOR]

In this paper, I consider the relationship between coming to understand why something must be the case and coming to understand why it actually is the case in some particular instance. Peter Lipton uses the possibility of coming to understand a phenomenon via a necessity proof as an argument that there can be understanding with no explanation. Lipton's argument has come under criticism, at least partially because one might think that understanding why something must be the case has a different object from understanding the actual phenomenon. In this paper, I argue that that under certain circumstances it is fairly straightforward to come to understand an actual phenomenon on the basis of knowing why it had to be the case. Rather than relying on brute intuitions, my primary strategy will be to show that on least some modern accounts of understanding we can validate Lipton's general point with respect to a variety of possible necessity relations. [ABSTRACT FROM AUTHOR]

Given the pervasiveness of AI systems and their potential negative effects on people's lives (especially among already marginalised groups), it becomes imperative to comprehend what goes on when an AI system generates a result, and based on what reasons, it is achieved. There are consistent technical efforts for making systems more "explainable" by reducing their opaqueness and increasing their interpretability and explainability. In this paper, we explore an alternative non-technical approach towards explainability that complement existing ones. Leaving aside technical, statistical, or data-related issues, we focus on the very conceptual underpinnings of the design decisions made by developers and other stakeholders during the lifecycle of a machine learning project. For instance, the design and development of an app to track snoring to detect possible health risks presuppose some picture or another of "health", which is a key notion that conceptually underpins the project. We take it as a premise that these key concepts are necessarily present during design and development, albeit perhaps tacitly. We argue that by providing "justificatory explanations" about how the team understands the relevant key concepts behind its design decisions, interested parties could gain valuable insights and make better sense of the workings and outcomes of systems. Using the concept of "health", we illustrate how a particular understanding of it might influence decisions during the design and development stages of a machine learning project, and how making this explicit by incorporating it into ex-post explanations might increase the explanatory and justificatory power of these explanations. We posit that a greater conceptual awareness of the key concepts that underpin design and development decisions may be beneficial to any attempt to develop explainability methods. We recommend that "justificatory explanations" are provided as technical documentation. These are declarative statements that contain at its simplest: (1) a high-level account of the understanding of the relevant key concepts a team possess related to a project's main domain, (2) how these understandings drive decision-making during the life-cycle stages, and (3) it gives reasons (which could be implicit in the account) that the person or persons doing the explanation consider to have plausible justificatory power for the decisions that were made during the project. [ABSTRACT FROM AUTHOR]

In 1956, John G. Kemeny and Paul Oppenheim proposed an approach to intertheoretical reduction as an alternative to that of Ernest Nagel. However, they neglected to provide a clear definition of its basic concept of systematization. After decades of languishing in the shadows, new interest in the KO approach is emerging. Nevertheless, there are still misunderstandings regarding this basic concept. The present paper elucidates this concept by returning to Oppenheim's hitherto little-noticed publications from the 1920s and 1930s, which Kemeny and Oppenheim obviously used as guidance in 1956. Reappraising Oppenheim's early writings delivers two significant payoffs: new clarity in understanding the concept of systematization as well as a more solid grasp of the structure of this approach as a distinctive combination of explanation and systematization. • The concept of systematization in the 1956 KO approach to intertheoretical reduction can be explicated with Oppenheim's philosophy of science from the 1920s and 1930s. • The degree of systematization of a theory is measured by the number of its terms. • There is a direct connection between systematization and explanation. • To explain means to describe something with fewer terms, i.e. to merge a summative juxtaposition of statements into one systematic law. [ABSTRACT FROM AUTHOR]

Despite a sea of interpretability methods that can produce plausible explanations, the field has also empirically seen many failure cases of such methods. In light of these results, it remains unclear for practitioners how to use these methods and choose between them in a principled way. In this paper, we show that for moderately rich model classes (easily satisfied by neural networks), any feature attribution method that is complete and linear—for example, Integrated Gradients and Shapley Additive Explanations (SHAP)—can provably fail to improve on random guessing for inferring model behavior. Our results apply to common end-tasks such as characterizing local model behavior, identifying spurious features, and algorithmic recourse. One takeaway from our work is the importance of concretely defining end-tasks: Once such an end-task is defined, a simple and direct approach of repeated model evaluations can outperform many other complex feature attribution methods. [ABSTRACT FROM AUTHOR]

What grounds the facts about what is identical to/distinct from what? A natural answer is: the facts about what exists. Despite its prima facie appeal, this view has received surprisingly little attention in the literature. Moreover, those who have discussed it have been inclined to reject it because of the following important challenge: why should the existence of some individuals ground their identity in some cases and their distinctness in others? (Burgess 2012, Shumener 2020b). This paper offers a sustained defense of the view. The first half provides some positive motivations in terms of other natural principles involving ground. The second half considers various ways of distilling the challenge into a precise objection to the view, and argues that none of the resulting objections proves persuasive. [ABSTRACT FROM AUTHOR]

In many practical situations, we observe a special Burr probability distribution — e.g., Burr distribution describes the distribution of people by income, it describes the rainfall data, etc. In this paper, we provide a theoretical explanation for the ubiquity of Burr distributions: namely, we show that this distribution naturally follows from scale-invariance. To be more precise, the simplest distribution that can be obtained from scale invariance is the Pareto distribution — a frequent particular case of the general Burr family of distributions. Next simplest are all distributions from the Burr family. We also use the scale-invariance approach to come up with a more general class of distributions that will, hopefully, provide an even more accurate description of the corresponding phenomena. [ABSTRACT FROM AUTHOR]

In general, the probability of the next state depends not only on the current state, but also on the whole pre-history. However, in practice, many real-life processes — including economic processes — can be well described by so-called Markov models, i.e., models in which the probability of the next state depends only on the current state. In this paper, we explain the empirical ubiquity of such models. [ABSTRACT FROM AUTHOR]

Post-election reviews provide an opportunity for political parties to diagnose the reasons for their success or failure. Since 2019, the reviews conducted by the Australian Labor and Liberal parties have been made public, and they provide an ideal opportunity to test their explanations against the evidence. This paper identifies six explanations for the 2022 Australian federal election outcome and tests them using the 2022 Australian Election Study survey. Both reviews correctly identify the importance of leadership and the pandemic in shaping the election result but underplay the importance of independents and of climate change. Both reviews over-estimate changes in voting among women and immigrants. Overall, the reviews are only partially accurate in explaining the election result. [ABSTRACT FROM AUTHOR]

What is computational explanation? Many accounts treat it as a kind of causal explanation. I argue against two more specific versions of this view, corresponding to two popular treatments of causal explanation. The first holds that computational explanation is mechanistic, while the second holds that it is interventionist. However, both overlook an important class of computational explanations, which I call limitative explanations. Limitative explanations explain why certain problems cannot be solved computationally, either in principle or in practice. I argue that limitative explanations are not plausibly understood in either mechanistic or interventionist terms. One upshot of this argument is that there are causal and non-causal kinds of computational explanation. I close the paper by suggesting that both are grounded in the notion of computational implementation. [ABSTRACT FROM AUTHOR]

Antirealism about metaphysical explanation is relatively underexplored. This paper maps out the territory for the antirealist, explaining what it would take to be an antirealist given various different conceptions of metaphysical explanation, and of the relationship between metaphysical explanation and grounding. [ABSTRACT FROM AUTHOR]

There is a widespread philosophical interpretation of natural selection in evolutionary theory: natural selection, like mutation, migration, and drift are seen as forces that propel the evolution of populations. Natural selection is thus a population level causal process. This account has been challenged by the Statistics, claiming that natural selection is not a population level cause but rather a statistical feature of a population. This paper examines the nature of the aforementioned ontological debate and the nature of statistical explanations given by population genetics. I claim that the Modern Synthesis provides good explanations of the changes in trait structure of populations without appealing to detailed causal information about the individual trajectories of the members of a population. [ABSTRACT FROM AUTHOR]

Based on the historical sense of Zarathustra I study a problem that goes through the entire tradition, namely, the problem of dualisms, the philosophical explanation that tends towards a split or, in other words, the proposal of a radical whole's separation in elements (soul-body, subject-object, truth-falsehood and so on). This paper doesn't just analyse a few dualisms, but it shows the duality as the inception of dualist thoughts and suggests how it is rather possible to give reason of dualisms by means of the unity. [ABSTRACT FROM AUTHOR]