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  1. Revisiting the narrow latent scope bias in explanatory reasoning.Simon Stephan - 2023 - Cognition 241 (C):105630.
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  • Best-System Laws, Explanation, and Unification.Thomas Blanchard - 2023 - In Christian Loew, Siegfried Jaag & Michael Townsen Hicks (eds.), Humean Laws for Human Agents. Oxford: Oxford UP.
    In recent years, an active research program has emerged that aims to develop a Humean best-system account (BSA) of laws of nature that improves on Lewis’s canonical articulation of the view. Its guiding idea is that the laws are cognitive tools tailored to the specific needs and limitations of creatures like us. While current versions of this “pragmatic Humean” research program fare much better than Lewis’s account along many dimensions, I will argue that they have trouble making sense of certain (...)
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  • (1 other version)Expressivism about explanatory relevance.Josh Hunt - 2024 - Philosophical Studies 181 (9):2063-2089.
    Accounts of scientific explanation disagree about what’s required for a cause, law, or other fact to be a reason why an event occurs. In short, they disagree about the conditions for explanatory relevance. Nonetheless, most accounts presuppose that claims about explanatory relevance play a descriptive role in tracking reality. By rejecting the need for this descriptivist assumption, I develop an expressivist account of explanatory relevance and explanation: to judge that an answer is explanatory is to express an attitude of _being (...)
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  • Symmetry and Reformulation: On Intellectual Progress in Science and Mathematics.Josh Hunt - 2022 - Dissertation, University of Michigan
    Science and mathematics continually change in their tools, methods, and concepts. Many of these changes are not just modifications but progress---steps to be admired. But what constitutes progress? This dissertation addresses one central source of intellectual advancement in both disciplines: reformulating a problem-solving plan into a new, logically compatible one. For short, I call these cases of compatible problem-solving plans "reformulations." Two aspects of reformulations are puzzling. First, reformulating is often unnecessary. Given that we could already solve a problem using (...)
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  • AI, Opacity, and Personal Autonomy.Bram Vaassen - 2022 - Philosophy and Technology 35 (4):1-20.
    Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to receive a (...)
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  • The Automated Laplacean Demon: How ML Challenges Our Views on Prediction and Explanation.Sanja Srećković, Andrea Berber & Nenad Filipović - 2021 - Minds and Machines 32 (1):159-183.
    Certain characteristics make machine learning a powerful tool for processing large amounts of data, and also particularly unsuitable for explanatory purposes. There are worries that its increasing use in science may sideline the explanatory goals of research. We analyze the key characteristics of ML that might have implications for the future directions in scientific research: epistemic opacity and the ‘theory-agnostic’ modeling. These characteristics are further analyzed in a comparison of ML with the traditional statistical methods, in order to demonstrate what (...)
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  • The Compact Compendium of Experimental Philosophy.Alexander Max Bauer & Stephan Kornmesser (eds.) - 2023 - Berlin and Boston: De Gruyter.
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  • What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research.Markus Langer, Daniel Oster, Timo Speith, Lena Kästner, Kevin Baum, Holger Hermanns, Eva Schmidt & Andreas Sesing - 2021 - Artificial Intelligence 296 (C):103473.
    Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these “stakeholders' desiderata”) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability (...)
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  • What's the Point of Understanding?Michael Hannon - 2019 - In What's the Point of Knowledge? A Function-First Epistemology. New York, NY, USA: Oxford University Press.
    What is human understanding and why should we care about it? I propose a method of philosophical investigation called ‘function-first epistemology’ and use this method to investigate the nature and value of understanding-why. I argue that the concept of understanding-why serves the practical function of identifying good explainers, which is an important role in the general economy of our concepts. This hypothesis sheds light on a variety of issues in the epistemology of understanding including the role of explanation, the relationship (...)
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  • Experiential Explanation.Sara Aronowitz & Tania Lombrozo - 2020 - Topics in Cognitive Science 12 (4):1321-1336.
    People often answer why-questions with what we call experiential explanations: narratives or stories with temporal structure and concrete details. In contrast, on most theories of the epistemic function of explanation, explanations should be abstractive: structured by general relationships and lacking extraneous details. We suggest that abstractive and experiential explanations differ not only in level of abstraction, but also in structure, and that each form of explanation contributes to the epistemic goals of individual learners and of science. In particular, experiential explanations (...)
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  • Do mathematical explanations have instrumental value?Rebecca Lea Morris - 2019 - Synthese (2):1-20.
    Scientific explanations are widely recognized to have instrumental value by helping scientists make predictions and control their environment. In this paper I raise, and provide a first analysis of, the question whether explanatory proofs in mathematics have analogous instrumental value. I first identify an important goal in mathematical practice: reusing resources from existing proofs to solve new problems. I then consider the more specific question: do explanatory proofs have instrumental value by promoting reuse of the resources they contain? In general, (...)
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  • Determinants of judgments of explanatory power: Credibility, Generality, and Statistical Relevance.Matteo Colombo, Leandra Bucher & Jan Sprenger - 2017 - Frontiers in Psychology:doi:10.3389/fpsyg.2017.01430.
    Explanation is a central concept in human psychology. Drawing upon philosophical theories of explanation, psychologists have recently begun to examine the relationship between explanation, probability and causality. Our study advances this growing literature in the intersection of psychology and philosophy of science by systematically investigating how judgments of explanatory power are affected by the prior credibility of a potential explanation, the causal framing used to describe the explanation, the generalizability of the explanation, and its statistical relevance for the evidence. Collectively, (...)
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  • Eight Other Questions about Explanation.Angela Potochnik - 2018 - In Alexander Reutlinger & Juha Saatsi (eds.), Explanation Beyond Causation: Philosophical Perspectives on Non-Causal Explanations. Oxford, United Kingdom: Oxford University Press.
    The tremendous philosophical focus on how to characterize explanatory metaphysical dependence has eclipsed a number of other unresolved issued about scientific explanation. The purpose of this paper is taxonomical. I will outline a number of other questions about the nature of explanation and its role in science—eight, to be precise—and argue that each is independent. All of these topics have received some philosophical attention, but none nearly so much as it deserves. Furthermore, existing views on these topics have been obscured (...)
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  • Stability, breadth and guidance.Thomas Blanchard, Nadya Vasilyeva & Tania Lombrozo - 2018 - Philosophical Studies 175 (9):2263-2283.
    Much recent work on explanation in the interventionist tradition emphasizes the explanatory value of stable causal generalizations—i.e., causal generalizations that remain true in a wide range of background circumstances. We argue that two separate explanatory virtues are lumped together under the heading of `stability’. We call these two virtues breadth and guidance respectively. In our view, these two virtues are importantly distinct, but this fact is neglected or at least under-appreciated in the literature on stability. We argue that an adequate (...)
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  • (1 other version)How Thought Experiments Increase Understanding.Michael T. Stuart - 2017 - In Michael T. Stuart, Yiftach Fehige & James Robert Brown (eds.), The Routledge Companion to Thought Experiments. London: Routledge. pp. 526-544.
    We might think that thought experiments are at their most powerful or most interesting when they produce new knowledge. This would be a mistake; thought experiments that seek understanding are just as powerful and interesting, and perhaps even more so. A growing number of epistemologists are emphasizing the importance of understanding for epistemology, arguing that it should supplant knowledge as the central notion. In this chapter, I bring the literature on understanding in epistemology to bear on explicating the different ways (...)
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  • The heuristic conception of inference to the best explanation.Finnur Dellsén - 2017 - Philosophical Studies 175 (7):1745-1766.
    An influential suggestion about the relationship between Bayesianism and inference to the best explanation holds that IBE functions as a heuristic to approximate Bayesian reasoning. While this view promises to unify Bayesianism and IBE in a very attractive manner, important elements of the view have not yet been spelled out in detail. I present and argue for a heuristic conception of IBE on which IBE serves primarily to locate the most probable available explanatory hypothesis to serve as a working hypothesis (...)
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  • Experimental Philosophy of Explanation Rising: The Case for a Plurality of Concepts of Explanation.Matteo Colombo - 2017 - Cognitive Science 41 (2):503-517.
    This paper brings together results from the philosophy and the psychology of explanation to argue that there are multiple concepts of explanation in human psychology. Specifically, it is shown that pluralism about explanation coheres with the multiplicity of models of explanation available in the philosophy of science, and it is supported by evidence from the psychology of explanatory judgment. Focusing on the case of a norm of explanatory power, the paper concludes by responding to the worry that if there is (...)
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  • Contrastive Constraints Guide Explanation‐Based Category Learning.Seth Chin-Parker & Julie Cantelon - 2017 - Cognitive Science 41 (6):1645-1655.
    This paper provides evidence for a contrastive account of explanation that is motivated by pragmatic theories that recognize the contribution that context makes to the interpretation of a prompt for explanation. This study replicates the primary findings of previous work in explanation-based category learning, extending that work by illustrating the critical role of the context in this type of learning. Participants interacted with items from two categories either by describing the items or explaining their category membership. We manipulated the feature (...)
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  • Explanation and explanationism in science and metaphysics.Juha Saatsi - 2017 - In Matthew H. Slater & Zanja Yudell (eds.), Metaphysics and the Philosophy of Science: New Essays. New York, NY, USA: Oxford University Press.
    This chapter examines the status of inference to the best explanation in naturalistic metaphysics. The methodology of inference to the best explanation in metaphysics is studied from the perspective of contemporary views on scientific explanation and explanatory inferences in the history and philosophy of science. This reveals serious shortcomings in prevalent attempts to vindicate metaphysical "explanationism" by reference to similarities between science and naturalistic metaphysics. This critique is brought out by considering a common gambit of methodological unity: (1) Both metaphysics (...)
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  • The Epistemology of Causal Selection: Insights from Systems Biology.Beckett Sterner - forthcoming - In C. Kenneth Waters & James Woodward (eds.), Philosophical Perspectives on Causal Reasoning in Biology. University of Minnesota Press.
    Among the many causes of an event, how do we distinguish the important ones? Are there ways to distinguish among causes on principled grounds that integrate both practical aims and objective knowledge? Psychologist Tania Lombrozo has suggested that causal explanations “identify factors that are ‘exportable’ in the sense that they are likely to subserve future prediction and intervention” (Lombrozo 2010, 327). Hence portable causes are more important precisely because they provide objective information to prediction and intervention as practical aims. However, (...)
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  • Relative explainability and double standards in medical decision-making: Should medical AI be subjected to higher standards in medical decision-making than doctors?Saskia K. Nagel, Jan-Christoph Heilinger & Hendrik Kempt - 2022 - Ethics and Information Technology 24 (2):20.
    The increased presence of medical AI in clinical use raises the ethical question which standard of explainability is required for an acceptable and responsible implementation of AI-based applications in medical contexts. In this paper, we elaborate on the emerging debate surrounding the standards of explainability for medical AI. For this, we first distinguish several goods explainability is usually considered to contribute to the use of AI in general, and medical AI in specific. Second, we propose to understand the value of (...)
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  • Principled Mechanistic Explanations in Biology: A Case Study of Alzheimer's Disease.Sepehr Ehsani - manuscript
    Following an analysis of the state of investigations and clinical outcomes in the Alzheimer's research field, I argue that the widely-accepted 'amyloid cascade' mechanistic explanation of Alzheimer's disease appears to be fundamentally incomplete. In this context, I propose that a framework termed 'principled mechanism' (PM) can help with remedying this problem. First, using a series of five 'tests', PM systematically compares different components of a given mechanistic explanation against a paradigmatic set of criteria, and hints at various ways of making (...)
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  • (1 other version)Data science and molecular biology: prediction and mechanistic explanation.Ezequiel López-Rubio & Emanuele Ratti - 2019 - Synthese (4):1-26.
    In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in (...)
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  • Bayesian Occam's Razor Is a Razor of the People.Thomas Blanchard, Tania Lombrozo & Shaun Nichols - 2018 - Cognitive Science 42 (4):1345-1359.
    Occam's razor—the idea that all else being equal, we should pick the simpler hypothesis—plays a prominent role in ordinary and scientific inference. But why are simpler hypotheses better? One attractive hypothesis known as Bayesian Occam's razor is that more complex hypotheses tend to be more flexible—they can accommodate a wider range of possible data—and that flexibility is automatically penalized by Bayesian inference. In two experiments, we provide evidence that people's intuitive probabilistic and explanatory judgments follow the prescriptions of BOR. In (...)
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  • Explanatory Judgment, Probability, and Abductive Inference.Matteo Colombo, Marie Postma & Jan Sprenger - 2016 - In A. Papafragou, D. Grodner, D. Mirman & J. C. Trueswell (eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 432-437) Cognitive Science Society. Cognitive Science Society. pp. 432-437.
    Abductive reasoning assigns special status to the explanatory power of a hypothesis. But how do people make explanatory judgments? Our study clarifies this issue by asking: How does the explanatory power of a hypothesis cohere with other cognitive factors? How does probabilistic information affect explanatory judgments? In order to answer these questions, we conducted an experiment with 671 participants. Their task was to make judgments about a potentially explanatory hypothesis and its cognitive virtues. In the responses, we isolated three constructs: (...)
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  • Explanatory Value and Probabilistic Reasoning: An Empirical Study.Matteo Colombo, Marie Postma & Jan Sprenger - 2016 - Proceedings of the Cognitive Science Society.
    The relation between probabilistic and explanatory reasoning is a classical topic in philosophy of science. Most philosophical analyses are concerned with the compatibility of Inference to the Best Explanation with probabilistic, Bayesian inference, and the impact of explanatory considerations on the assignment of subjective probabilities. This paper reverses the question and asks how causal and explanatory considerations are affected by probabilistic information. We investigate how probabilistic information determines the explanatory value of a hypothesis, and in which sense folk explanatory practice (...)
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  • No brute facts: The Principle of Sufficient Reason in ordinary thought.Scott Partington, Alejandro Vesga & Shaun Nichols - 2023 - Cognition 238 (C):105479.
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  • Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard?John Zerilli, Alistair Knott, James Maclaurin & Colin Gavaghan - 2018 - Philosophy and Technology 32 (4):661-683.
    We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and explainability are certainly important desiderata in algorithmic governance, we worry that automated decision-making is being held to an unrealistically high standard, possibly owing to an unrealistically high estimate of the degree of transparency attainable from human decision-makers. In this paper, we review evidence demonstrating that much human decision-making is fraught with transparency problems, show in what respects AI fares little worse or better and argue that (...)
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  • Representing, Running, and Revising Mental Models: A Computational Model.Scott Friedman, Kenneth Forbus & Bruce Sherin - 2018 - Cognitive Science 42 (4):1110-1145.
    People use commonsense science knowledge to flexibly explain, predict, and manipulate the world around them, yet we lack computational models of how this commonsense science knowledge is represented, acquired, utilized, and revised. This is an important challenge for cognitive science: Building higher order computational models in this area will help characterize one of the hallmarks of human reasoning, and it will allow us to build more robust reasoning systems. This paper presents a novel assembled coherence theory of human conceptual change, (...)
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  • Folk attributions of understanding: Is there a role for epistemic luck?Daniel A. Wilkenfeld, Dillon Plunkett & Tania Lombrozo - 2018 - Episteme 15 (1):24-49.
    As a strategy for exploring the relationship between understanding and knowledge, we consider whether epistemic luck – which is typically thought to undermine knowledge – undermines understanding. Questions about the etiology of understanding have also been at the heart of recent theoretical debates within epistemology. Kvanvig (2003) put forward the argument that there could be lucky understanding and produced an example that he deemed persuasive. Grimm (2006) responded with a case that, he argued, demonstrated that there could not be lucky (...)
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  • Functions and Cognitive Bases for the Concept of Actual Causation.David Danks - 2013 - Erkenntnis 78 (1):111-128.
    Our concept of actual causation plays a deep, ever-present role in our experiences. I first argue that traditional philosophical methods for understanding this concept are unlikely to be successful. I contend that we should instead use functional analyses and an understanding of the cognitive bases of causal cognition to gain insight into the concept of actual causation. I additionally provide initial, programmatic steps towards carrying out such analyses. The characterization of the concept of actual causation that results is quite different (...)
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  • (1 other version)Data science and molecular biology: prediction and mechanistic explanation.Ezequiel López-Rubio & Emanuele Ratti - 2021 - Synthese 198 (4):3131-3156.
    In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in (...)
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