Organizations such as the EU High-Level Expert Group on AI and the IEEE have recently formulated ethical principles and (moral) values that should be adhered to in the design and deployment of artificial intelligence (AI). These include respect for autonomy, non-maleficence, fairness, transparency, explainability, and accountability. But how can we ensure and verify that an AI system actually respects these values? To help answer this question, I propose an account for determining when an AI system can be said to embody (...) certain values. This account understands embodied values as the result of design activities intended to embed those values in such systems. AI systems are here understood as a special kind of sociotechnical system that, like traditional sociotechnical systems, are composed of technical artifacts, human agents, and institutions but—in addition—contain artificial agents and certain technical norms that regulate interactions between artificial agents and other elements of the system. The specific challenges and opportunities of embedding values in AI systems are discussed, and some lessons for better embedding values in AI systems are drawn. (shrink)

Issues concerning scientific explanation have been a focus of philosophical attention from Pre- Socratic times through the modern period. However, recent discussion really begins with the development of the Deductive-Nomological (DN) model. This model has had many advocates (including Popper 1935, 1959, Braithwaite 1953, Gardiner, 1959, Nagel 1961) but unquestionably the most detailed and influential statement is due to Carl Hempel (Hempel 1942, 1965, and Hempel & Oppenheim 1948). These papers and the reaction to them have structured subsequent discussion concerning (...) scientific explanation to an extraordinary degree. After some general remarks by way of background and orientation (Section 1), this entry describes the DN model and its extensions, and then turns to some well-known objections (Section 2). It next describes a variety of subsequent attempts to develop alternative models of explanation, including Wesley Salmon's Statistical Relevance (Section 3) and Causal Mechanical (Section 4) models and the Unificationist models due to Michael Friedman and Philip Kitcher (Section 5). Section 6 provides a summary and discusses directions for future work. (shrink)

This book considers the question: to what extent does it make sense to qualify technical artefacts as moral entities? The authors’ contributions trace recent proposals and topics including instrumental and non-instrumental values of artefacts, agency and artefactual agency, values in and around technologies, and the moral significance of technology. The editors’ introduction explains that as ‘agents’ rather than simply passive instruments, technical artefacts may actively influence their users, changing the way they perceive the world, the way they act in the (...) world and the way they interact with each other. This volume features the work of various experts from around the world, representing a variety of positions on the topic. Contributions explore the contested discourse on agency in humans and artefacts, defend the Value Neutrality Thesis by arguing that technological artefacts do not contain, have or exhibit values, or argue that moral agency involves both human and non-human elements. The book also investigates technological fields that are subject to negative moral valuations due to the harmful effects of some of their products. It includes an analysis of some difficulties arising in Artificial Intelligence and an exploration of values in Chemistry and in Engineering. The Moral Status of Technical Artefacts is an advanced exploration of the various dimensions of the relations between technology and morality. (shrink)

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is (...) “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®. (shrink)

Laurent Stern here provides a concise account of the difficulties that arise within the interpretive process and in the context of interpretive conflict. Speakers and agents are expected by others to be occasionally insincere. Attempting to be tolerant of alternative interpretations, and dealing with the insincerity of others, often motivates interpreters themselves to become insincere. Accordingly, moral issues emerge for both speakers and interpreters. Interpretive Reasoning discusses such issues in the literature on interpretation. Stern offers a carefully argued account of (...) the very idea of interpretation. What are the constraints on interpretations? What are our grounds for demanding that others agree with our interpretations? How do we support our interpretations? What are the types of interpretations we encounter? How are problems of first-person authority and self-knowledge connected with interpreting? While the author argues for interpretations supported by principles rather than by the consensus of interpreters, he also shows that even well-supported interpretations may be mistaken, and that some interpretive conflicts are interminable. Although this is a book in philosophy, scholars and students in the humanities, the social sciences, and disciplines concerned with interpretive reasoning can read it profitably. (shrink)

Recently, many have argued that there are certain kinds of abstract mathematical explanations that are noncausal. In particular, the irrelevancy approach suggests that abstracting away irrelevant causal details can leave us with a noncausal explanation. In this paper, I argue that the common example of Renormalization Group explanations of universality used to motivate the irrelevancy approach deserves more critical attention. I argue that the reasons given by those who hold up RG as noncausal do not stand up to critical scrutiny. (...) As a result, the irrelevancy approach and the line between casual and noncausal explanation deserves more scrutiny. (shrink)

Although this is a book in philosophy, scholars and students in the humanities, the social sciences, and disciplines concerned with interpretive reasoning can ...

The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes a robust (...) argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the "reviewed view" of scientific explanation to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts. (shrink)

Today, there is an emerging interest for the potential role of hermeneutics in reflecting on the practices related to digital technologies and their consequences. Nonetheless, such an interest has neither given rise to a unitary approach nor to a shared debate. The primary goal of this paper is to map and synthetize the different existing perspectives to pave the way for an open discussion on the topic. The article is developed in two steps. In the first section, the authors analyze (...) digital hermeneutics “in theory” by confronting and systematizing the existing literature. In particular, they stress three main distinctions among the approaches: between “methodological” and “ontological” digital hermeneutics; between data- and text-oriented digital hermeneutics; and between “quantitative” and “qualitative” credos in digital hermeneutics. In the second section, they consider digital hermeneutics “in action”, by critically analyzing the uses of digital data for studying a classical object such as the political opinion. In the conclusion, we will pave the way to an ontological turn in digital hermeneutics. Most of this article is devoted to the methodological issue of interpreting with digital machines. The main task of an ontological digital hermeneutics would consist instead in wondering if it is legitimate, and eventually to which extent, to speak of digital technologies, or at least of some of them, as interpretational machines. (shrink)

Renormalization group (RG) methods are an established strategy to explain how it is possible that microscopically different systems exhibit virtually the same macro behavior when undergoing phase-transitions. I argue – in agreement with Robert Batterman – that RG explanations are non-causal explanations. However, Batterman misidentifies the reason why RG explanations are non-causal: it is not the case that an explanation is non- causal if it ignores causal details. I propose an alternative argument, according to which RG explanations are non-causal explanations (...) because their explanatory power is due to the application mathematical operations, which do not serve the purpose of representing causal relations. (shrink)

In this paper I critically examine the notion of explanation used in Artificial Intelligence in general, and in the theory of belief revision in particular. I focus on two of the best known accounts in the literature: Pagnucco’s abductive expansion functions and Gärdenfors’ counterfactual analysis. I argue that both accounts are at odds with the way in which this notion has historically been understood in philosophy. They are also at odds with the explanatory strategies used in actual scientific practice. At (...) the end of the paper I outline the desiderata for an epistemologically motivated, scientifically informed belief revision model for explanation. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:JOSEPH MARGOLIS THE LOGIC AND STRUCTURES OF FICTIONAL NARRATIVE The fascination of fiction and narrative is plainly immense, sind current analyses are notably fresh and ingenious. But ifone were to venture a compendious account of die most strategic conceptual claims bearing on those notions, they might well be captured by the following three theses: (i) that fiction and narrative are logically quite distinct, without necessarily excluding one anodier; (ii) (...) that fictional worlds are no worlds at all; they are merely imagined to exist; and (iii) that events, whether actual or imagined, cannot be specified independently of the categories by which we identify them as the events they are, categories subject to change over time. What is intriguing about mese three claims is that they strike the ear as utterly commonplace and obvious. But that is dieir strength. For it would be a great convenience if the relevant puzzles generated by analytic philosophy, hermeneutics, the new narratology, semiotics and the like could be resolved, convincingly, by appeal to die implications of these diree homely trudis. In addition, diey affect one another in a rather complicated way, and, taken joindy, easily expose certain influential false starts. The import of (iii), for example, is notably ignored in die otherwise powerful analysis of narrative afforded by Gérard Genette. Its bearing on (i) utterly undermines the explicit dualism favored by Seymour Chatman, who is clearly influenced by Genette himself. The failure to mark die force of (i) and its analogue regarding poetry is most obvious in I. A. Richards's contrast between poetry and science, the work of the Prague circle, Northrop Frye's Anatomy of Criticism, and such recent speech-act theories of poetry and fiction as those proposed by Richard Ohmann, Monroe Beardsley, and Mary Louise Pratt. The corresponding failure widi respect to (ii) is clear in John Searle's theory of reference and in die ontological claims made by Nicholas Wolterstorff. The connection between (i) and (ii) is muddled most famously by G. E. Moore. These charges suggest die quarrelsome promise of the intended economy. But, as we shall see, they also lead in the direction of a more constructive theory of texts and their interpretation and of die constraints of narrative and style diat affect fiction. 162 Joseph Margolis163 Fiction, we say, differs from reality as well as from legend. But in speaking thus, we betray an understandable confidence that these three kinds of"world"— or, these three worlds — are conceptually well-behaved and open to straightforward demarcation. Once more, we slip altogether too easily between talking about certain worlds and about the narrative accounts by which we do so. What distinguishes fictional reference? we ask, and what is a fictional world? The best way to proceed here is to begin with strong intuitions and to trace dieir systematic consequences in order to test how far they are unassailable — or whether and where they must yield to others that are more accommodating. One particularly promising equivocation is the following. We speak of fictional worlds as possible (but not actual) worlds; but even if what is possible in a fictional world corresponds (in some sense) to what is possible in the actual world, it is not, as such, a possibility of or at the actual world. The "possible worlds" idiom is fundamentally and doubly equivocal. For, a "possible world" signifies either what is merely logically or conceptually compossible, or what (under suitably referential, predicative, causal, and similar constraints) is compossible specifically with respect to the actual world (or some part of it). Certainly, Balzac's La Cousine Bette "presents" an imaginary world that is internally coherent and compossible. One might even say, ignoring its fictive nature, diat such a world — or events very much like those of the story — is compossible with a certain portion of the actual world of nineteenth-century France. But Lewis Carroll's Alice in Wonderfond, though it scrupulously presents a coherent world of compossible events, does not present a world compossible with the actual world. And, on certain views at least, Thomas Mann's The Transposed Heads presents a possible world that is a conceptual impossibility. There is, however, no need to quarrel about particular cases... (shrink)

In this paper, we explore how the development and affordances of predictive analytics may impact how teachers and other educational actors think about and teach students and, more broadly, how society understands education. Our particular focus is on the data dashboards of learning support systems which are based on Machine Learning. While previous research has focused on how these systems produce credible knowledge, we explore here how they also produce compelling, persuasive and convincing narratives. Our main argument is that particular (...) kinds of stories are written by predictive analytics and written into their data dashboards. Based on a case study of a leading predictive analytics system, we explore how data dashboards imply causality between the ‘facts’ they are visualising. To do so, we analyse the stories they tell according to their spatial and temporal dimensions, characters and events, sequentiality as well as tellability. In the stories we identify, teachers are managers, students are at greater or lesser risk, and students’ sociality is reduced to machine-readable interactions. Overall, only data marked as individual behaviours becomes relevant to the system, rendering structural inequalities invisible. Reflecting on the implications of these systems, we suggest ways in which the uptake of these systems can interrupt such stories and reshape them in other directions. (shrink)

The increasing use of Artificial Intelligence for making decisions in public affairs has sparked a lively debate on the benefits and potential harms of self-learning technologies, ranging from the hopes of fully informed and objectively taken decisions to fear for the destruction of mankind. To prevent the negative outcomes and to achieve accountable systems, many have argued that we need to open up the “black box” of AI decision-making and make it more transparent. Whereas this debate has primarily focused on (...) how transparency can secure high-quality, fair, and reliable decisions, far less attention has been devoted to the role of transparency when it comes to how the general public come to perceive AI decision-making as legitimate and worthy of acceptance. Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system. This paper discusses how transparency in and about AI decision-making can affect the public’s perception of the legitimacy of decisions and decision-makers and produce a framework for analyzing these questions. We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring. (shrink)

We propose a framework for including information‐processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the definition of (...) rational action. Theories are specified as optimal program problems, defined by an adaptation environment, a bounded machine, and a utility function. Such theories yield different classes of explanation, depending on the extent to which they emphasize adaptation to bounds, and adaptation to some ecology that differs from the immediate local environment. We illustrate this variation with examples from three domains: visual attention in a linguistic task, manual response ordering, and reasoning. We explore the relation of this framework to existing “levels” approaches to explanation, and to other optimality‐based modeling approaches. (shrink)

Varied evidence for a hypothesis confirms it more strongly than less varied evidence, ceteris paribus. This epistemological Variety of Evidence Thesis enjoys long-standing widespread intuitive support. Recent literature has raised serious doubts that the correlational approach of explicating the thesis can vindicate it. By contrast, the eliminative approach due to Horwich vindicates the Variety of Evidence Thesis but only within a relatively narrow domain. I investigate the prospects of extending the eliminative approach to a larger domain by considering a larger (...) class of sensible explications of evidential variety. For a large subclass class of such explications I show how to construct cases in which the less diverse body of evidence for a hypothesis confirms more strongly. I hence argue that these prospects are dire since the eliminative approach widely fails to vindicate the thesis. (shrink)

There has been a growing trend to include non-causal models in accounts of scientific explanation. A worry addressed in this paper is that without a higher threshold for explanation there are no tools for distinguishing between models that provide genuine explanations and those that provide merely potential explanations. To remedy this, a condition is introduced that extends a veridicality requirement to models that are empirically underdetermined, highly-idealised, or otherwise non-causal. This condition is applied to models of electroweak symmetry breaking beyond (...) the Standard Model. (shrink)

"This is a fine volume that clarifies, defends, and moves beyond the views that Kim presented in Mind in a Physical World.

Introduction: situating Heidegger and the philosophy of technology -- Heidegger's philosophy of technology -- The historical-ontological priority of technology over science -- Deromanticizing Heidegger -- Interlude: the earth inherited -- Was Heidegger prescient concerning technoscience? -- Heidegger's technologies: one size fits all -- Concluding postphenomenological postscript: writing technologies.

This paper argues that in some explanations mathematics are playing an explanatory rather than a representational role, and that this feature unifies many types of non-causal or non-mechanistic explanations that some philosophers of science have been recently exploring under various names. After showing how mathematics can play either a representational or an explanatory role by considering two alternative explanations of a same biological pattern—“Bergmann’s rule”—I offer an example of an explanation where the bulk of the explanatory job is done by (...) a mathematical theorem, and where mechanisms involved in the target systems are not explanatorily relevant. Then I account for the way mathematical properties may function in an explanatory way within an explanation by arguing that some mathematical propositions involving variables non directly referring to the target system features constitute constraints to which a whole class of systems should comply, provided they are describable by a mathematical object concerned by those propositions. According to such “constraint account”, those mathematical facts are directly entailing the explanandum, as a consequence of such constraints. I call those explanations “structural”, because here properties of mathematical structures are accounting for the explanandum; various kinds of mathematical structures thereby define various types of structural explanations. (shrink)

Should you be targeted by police for a crime that AI predicts you will commit? In this paper, we analyse when, and to what extent, the person-based predictive policing (PP) — using AI technology to identify and handle individuals who are likely to breach the law — could be justifiably employed. We first examine PP’s epistemological limits, and then argue that these defects by no means refrain from its usage; they are worse in humans. Next, based on major AI ethics (...) guidelines (IEEE, EU, and RIKEN, etc.), we refine three basic moral principles specific to person-based PP. We also derive further requirements from case studies, including debates in Chicago, New Orleans, San Francisco, Tokyo, and cities in China. Instead of rejecting PP programs, we analyse what necessary conditions should be met for using the tool to achieve social good. While acknowledging its risks, we conclude that the person-based PP could be beneficial in community policing, especially when merging into a larger governance framework of the social safety net. (shrink)

Discussions about a replicability crisis in science have been driven by the normative claim that all of science should be replicable and the empirical claim that most of it isn’t. Recently, such crisis talk has been challenged by a new localism, which argues a) that serious problems with replicability are not a general occurrence in science and b) that replicability itself should not be treated as a universal standard. The goal of this article is to introduce this emerging strand of (...) the debate and to discuss some of its implications and limitations. I will in particular highlight the issue of demarcation that localist accounts have to address, i.e. the question of how we can distinguish replicable science from disciplines where replicability does not apply. (shrink)

The standard taxonomy of theories of epistemic justification generates four positions from the Foundationalism v. Coherentism and Internalism v. Externalism disputes. I develop a new taxonomy driven by two other distinctions: Fundamentalism v. Non-Fundamentalism and Actual-Result v. Proper-Aim conceptions of epistemic justification. Actual-Result theorists hold that a belief is justified only if, as an actual matter of fact, it is held or formed in a way that makes it more likely than not to be true. Proper-Aim theorists hold that a (...) belief is justified only if it is held or formed in a way that it proper or correct insofar as truth is the aim or norm. Fundamentalists hold that which particular ways of holding or forming beliefs that confer justification is knowable a priori; epistemic principles are a priori necessary truths. Non- Fundamentalists disagree; epistemic principles are empirical contingent truths. The new taxonomy generates four positions: Cartesianism, Reliabilism, Intuitionism, and Pragmatism. The first two are Actual-Result; the second two are Proper-Aim. The first and third are Fundamentalist, the second and fourth are Non-Fundamentalist. The new taxonomy illuminates much of the current debate in the theory of epistemic justification. (shrink)

[Correction Notice: An erratum for this article was reported in Vol 109 of Psychological Review. Due to circumstances that were beyond the control of the authors, the studies reported in "Models of Ecological Rationality: The Recognition Heuristic," by Daniel G. Goldstein and Gerd Gigerenzer overlap with studies reported in "The Recognition Heuristic: How Ignorance Makes Us Smart," by the same authors and with studies reported in "Inference From Ignorance: The Recognition Heuristic". In addition, Figure 3 in the Psychological Review article (...) was originally published in the book chapter and should have carried a note saying that it was used by permission of Oxford University Press.] One view of heuristics is that they are imperfect versions of optimal statistical procedures considered too complicated for ordinary minds to carry out. In contrast, the authors consider heuristics to be adaptive strategies that evolved in tandem with fundamental psychological mechanisms. The recognition heuristic, arguably the most frugal of all heuristics, makes inferences from patterns of missing knowledge. This heuristic exploits a fundamental adaptation of many organisms: the vast, sensitive, and reliable capacity for recognition. The authors specify the conditions under which the recognition heuristic is successful and when it leads to the counter-intuitive less-is-more effect in which less knowledge is better than more for making accurate inferences. (shrink)

Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the (...) authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. (shrink)

COVID-19 has substantially affected our lives during 2020. Since its beginning, several epidemiological models have been developed to investigate the specific dynamics of the disease. Early COVID-19 epidemiological models were purely statistical, based on a curve-fitting approach, and did not include causal knowledge about the disease. Yet, these models had predictive capacity; thus they were used to ground important political decisions, in virtue of the understanding of the dynamics of the pandemic that they offered. This raises a philosophical question about (...) how purely statistical models can yield understanding, and if so, what the relationship between prediction and understanding in these models is. Drawing on the model that was developed by the Institute of Health Metrics and Evaluation, we argue that early epidemiological models yielded a modality of understanding that we call descriptive understanding, which contrasts with the so-called explanatory understanding which is assumed to be the main form of scientific understanding. We spell out the exact details of how descriptive understanding works, and efficiently yields understanding of the phenomena. Finally, we vindicate the necessity of studying other modalities of understanding that go beyond the conventionally assumed explanatory understanding. (shrink)

How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task [1]. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure. However, the original formulation of the apperception task had one fundamental limitation: it assumed (...) the raw sensory input had already been parsed using a set of discrete categories, so that all the system had to do was receive this already-digested symbolic input, and make sense of it. But what if we don't have access to pre-parsed input? What if our sensory sequence is raw unprocessed information? The central contribution of this paper is a neuro-symbolic framework for distilling interpretable theories out of streams of raw, unprocessed sensory experience. First, we extend the definition of the apperception task to include ambiguous (but still symbolic) input: sequences of sets of disjunctions. Next, we use a neural network to map raw sensory input to disjunctive input. Our binary neural network is encoded as a logic program, so the weights of the network and the rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules). (shrink)

The argument from causal closure of the physical is usually considered the most powerful argument in favor of the ontological doctrine of physicalism. Many authors, most notably Papineau, assume that CCP implies that physicalism is supported by physics. I demonstrate, however, that physical science has no bias in the ontological debate between proponents of physicalism and dualism. I show that the arguments offered for CCP are effective only against the accounts of mental causation based on the action of the mental (...) forces of a Newtonian nature, i.e. those which manifest themselves by causing accelerations. However, it is conceivable and possible that mental causation is manifested through the redistribution of energy, momentum and other conserved quantities in the system, brought about by altering the state probability distribution within the living system and leading to anomalous correlations of neural processes. After arguing that a probabilistic, interactionist model of mental causation is conceivable, which renders the argument from causal closure of the physical ineffective, I point to some basic features that such a model must have in order to be intelligible. At the same time, I indicate the way that conclusive testing of CCP can be done within the theoretical framework of physics. (shrink)

The argument from causal closure of the physical is usually considered the most powerful argument in favor of the ontological doctrine of physicalism. Many authors, most notably Papineau, assume that CCP implies that physicalism is supported by physics. I demonstrate, however, that physical science has no bias in the ontological debate between proponents of physicalism and dualism. I show that the arguments offered for CCP are effective only against the accounts of mental causation based on the action of the mental (...) forces of a Newtonian nature, i.e. those which manifest themselves by causing accelerations. However, it is conceivable and possible that mental causation is manifested through the redistribution of energy, momentum and other conserved quantities in the system, brought about by altering the state probability distribution within the living system and leading to anomalous correlations of neural processes. After arguing that a probabilistic, interactionist model of mental causation is conceivable, which renders the argument from causal closure of the physical ineffective, I point to some basic features that such a model must have in order to be intelligible. At the same time, I indicate the way that conclusive testing of CCP can be done within the theoretical framework of physics. (shrink)

The goal of this paper is to present and defend an empiricist, neo-Hempelian account of scientific explanation as ampliative, specialized embedding. The proposal aims to preserve what I take to be the core of Hempel’s empiricist account, by weakening it in some respects and strengthening it in others, introducing two new conditions that solve most of Hempel’s problems without abandoning his empiricist strictures. According to this proposal, to explain a phenomenon is to make it expectable by introducing new conceptual/ontological machinery (...) and using special, and non-ad hoc, non-accidental regularities. The new conditions are elaborated making essential use of two central structuralist ideas, namely T-theoreticity and specialization. I first introduce and qualify the project, then present the new account in detail and assess it vis-à-vis its rivals, and finally discuss some possible objections, concluding that the account fares better than its monistic rivals and well enough to qualify as a promising neo-Hempelian account. Even for those unpersuaded by its monistic goals, it has the merit of calling attention to two new necessary conditions not explicitly emphasized thus far and showing how they serve to answer many of the criticisms addressed against Hempel’s account. (shrink)

Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what is (...) required of an adequate mechanistic model. Mechanistic models are explanatory. (shrink)

The literature on self-driving cars and ethics continues to grow. Yet much of it focuses on ethical complexities emerging from an individual vehicle. That is an important but insufficient step towards determining how the technology will impact human lives and society more generally. What must complement ongoing discussions is a broader, system level of analysis that engages with the interactions and effects that these cars will have on one another and on the socio-technical systems in which they are embedded. To (...) bring the conversation of self-driving cars to the system level, we make use of two traffic scenarios which highlight some of the complexities that designers, policymakers, and others should consider related to the technology. We then describe three approaches that could be used to address such complexities and their associated shortcomings. We conclude by bringing attention to the “Moral Responsibility for Computing Artifacts: The Rules”, a framework that can provide insight into how to approach ethical issues related to self-driving cars. (shrink)

Fueled by ever-growing amounts of (digital) data and advances in artificial intelligence, decision-making in contemporary societies is increasingly delegated to automated processes. Drawing from social science theories and from the emerging body of research about algorithmic appreciation and algorithmic perceptions, the current study explores the extent to which personal characteristics can be linked to perceptions of automated decision-making by AI, and the boundary conditions of these perceptions, namely the extent to which such perceptions differ across media, (public) health, and judicial (...) contexts. Data from a scenario-based survey experiment with a national sample (N = 958) show that people are by and large concerned about risks and have mixed opinions about fairness and usefulness of automated decision-making at a societal level, with general attitudes influenced by individual characteristics. Interestingly, decisions taken automatically by AI were often evaluated on par or even better than human experts for specific decisions. Theoretical and societal implications about these findings are discussed. (shrink)

Reasoning in Biological Discoveries brings together a series of essays, which focus on one of the most heavily debated topics of scientific discovery. Collected together and richly illustrated, Darden's essays represent a groundbreaking foray into one of the major problems facing scientists and philosophers of science. Divided into three sections, the essays focus on broad themes, notably historical and philosophical issues at play in discussions of biological mechanism; and the problem of developing and refining reasoning strategies, including interfield relations and (...) anomaly resolution. Darden summarizes the philosophy of discovery and elaborates on the role that mechanisms play in biological discovery. Throughout the book, she uses historical case studies to extract advisory reasoning strategies for discovery. Examples in genetics, molecular biology, biochemistry, immunology, neuroscience and evolutionary biology reveal the process of discovery in action. (shrink)

Carl Craver investigates what we are doing when we sue neuroscience to explain what's going on in the brain.

During the past decade, social mechanisms and mechanism-based ex- planations have received considerable attention in the social sciences as well as in the philosophy of science. This article critically reviews the most important philosophical and social science contributions to the mechanism approach. The first part discusses the idea of mechanism- based explanation from the point of view of philosophy of science and relates it to causation and to the covering-law account of explanation. The second part focuses on how the idea (...) of mechanisms has been used in the social sciences. The final part discusses recent developments in analytical sociology, covering the nature of sociological explananda, the role of theory of action in mechanism-based explanations, Merton’s idea of middle-range theory, and the role of agent-based simulations in the development of mechanism-based explanations. (shrink)

En este trabajo me propongo desarrollar un estudio crítico de la concepción mecanicista de la explicación científica. En primer lugar, argumento que la caracterización mecanicista de los modelos fenoménicos (no explicativos) es inadecuada, pues no ofrece un análisis aceptable de los conceptos de modelo científico y similitud, que son fundamentales para la propuesta. En segundo lugar, sostengo que la caracterización de los modelos mecanicistas (explicativos) es igualmente inadecuada, pues los análisis disponibles de la relación explicativa de relevancia constitutiva implican una (...) tesis metafísica que es rechazada por los mismos mecanicistas. Concluyo que el mecanicismo no ofrece todavía una elucidación aceptable de la explicación científica. In this paper, I offer a critical assessment of the mechanicist approach to scientific explanation. Firstly, I argue that the mechanicist characterization of (non explanatory) phenomenological models is inadequate, since it does not develop an explication of the concepts of scientific model and similarity, which are indispensable to the approach. Secondly, I claim that the mechanicist conception of (explanatory) mechanicist models is inadequate as well, since all the available analices of the explanatory relation of constitutive relevance imply a metaphysical thesis that is rejected by the mechanicists themselves. I conclude that mechanicism needs to be emended if it aims to be considered as a genuinely illuminating approach to scientific explanation. (shrink)

Context: The problems that are most in need of interdisciplinary collaboration are “wicked problems,” such as food crises, climate change mitigation, and sustainable development, with many relevant aspects, disagreement on what the problem is, and contradicting solutions. Such complex problems both require and challenge interdisciplinarity. Problem: The conventional methods of interdisciplinary research fall short in the case of wicked problems because they remain first-order science. Our aim is to present workable methods and research designs for doing second-order science in domains (...) where there are many different scientific knowledges on any complex problem. Method: We synthesize and elaborate a framework for second-order science in interdisciplinary research based on a number of earlier publications, experiences from large interdisciplinary research projects, and a perspectivist theory of science. Results: The second-order polyocular framework for interdisciplinary research is characterized by five principles. Second-order science of interdisciplinary research must: 1. draw on the observations of first-order perspectives, 2. address a shared dynamical object, 3. establish a shared problem, 4. rely on first-order perspectives to see themselves as perspectives, and 5. be based on other rules than first-order research. Implications: The perspectivist insights of second-order science provide a new way of understanding interdisciplinary research that leads to new polyocular methods and research designs. It also points to more reflexive ways of dealing with scientific expertise in democratic processes. The main challenge is that this is a paradigmatic shift, which demands that the involved disciplines, at least to some degree, subscribe to a perspectivist view. Constructivist content: Our perspectivist approach to science is based on the second-order cybernetics and systems theories of von Foerster, Maruyama, Maturana & Varela, and Luhmann, coupled with embodied theories of cognition and semiotics as a general theory of meaning from von Uexküll and Peirce. (shrink)