Experimental studies have suggested that variables such as aptness or conventionality are predictors of people’s preference for expressing a particular topic–vehicle pair as either a metaphor or a simile. In the present study, we investigated if such variables would also be predictive within a more naturalistic context, where other variables, such as the intention to include an explanation, may also influence people’s decision. Specifically, we investigated the production of metaphor and simile expressions on the Internet via the Google search engine (...) and checked for accompanying explanations, as well as the properties they expressed, to examine whether ratings such as aptness, conventionality, as well as participants’ own stated preference or the intention to produce an explanation, would predict which topic–vehicle pairs appeared more often as metaphor. We found that participants’ stated preference predicted metaphor dominance on the Internet, and that apt topic–vehicles occurred more often as metaphors. The explanations collected, however, occurred 82% of the time after similes, and familiar expressions were the most explained. Finally, comparing the properties expressed in these explanations to normed property lists, we found that simile explanations typically expressed a novel conception of the topic–vehicle relationship. Therefore, we found that Internet posters use metaphors to convey an apt relationship, as found in previous laboratory studies, but prefer using a simile frame when they want to express a relationship that readers will find novel. (shrink)

Contents Preface General Introduction 1 | Science and Pseudoscience Introduction Karl Popper, Science: Conjectures and Refutations Thomas S. Kuhn, Logic of Discovery or Psychology of Research? Imre Lakatos, Science and Pseudoscience Paul R. Thagard, Why Astrology Is a Pseudoscience Michael Ruse, Creation-Science Is Not Science Larry Laudan, Commentary: Science at the Bar---Causes for Concern Commentary 2 | Rationality, Objectivity, and Values in Science Introduction Thomas S. Kuhn, The Nature and Necessity of Scientific Revolutions Thomas S. Kuhn, Objectivity, Value Judgment, and (...) Theory Choice Ernan McMullin, Rationality and Paradigm Change in Science Larry Laudan, Kuhn’s Critique of Methodology Helen E. Longino, Values and Objectivity Kathleen Okruhlik, Gender and the Biological Sciences Commentary 3 | The Duhem-Quine Thesis and Underdetermination Introduction Pierre Duhem, Physical Theory and Experiment W. V. Quine, Two Dogmas of Empiricism Donald Gillies, The Duhem Thesis and the Quine Thesis Larry Laudan, Demystifying Underdetermination *Colin Howson and Peter Urbach, The Duhem Problem Commentary 4 | Induction, Prediction, and Evidence Introduction Peter Lipton, Induction Karl Popper, The Problem of Induction Wesley C. Salmon, Rational Prediction Carl G. Hempel, Criteria of Confirmation and Acceptability Peter Achinstein, Explanation v. Prediction: Which Carries More Weight? *Nelson Goodman, The New Riddle of Induction Commentary 5 | Confirmation and Relevance: Bayesian Approaches Introduction Wesley C. Salmon, Rationality and Objectivity in Science *Deborah G. Mayo, A Critique of Salmon’s Bayesian Way *Alan Chalmers, The Bayesian Approach Paul Horwich, Therapeutic Bayesianism Commentary 6 | Models of Explanation Introduction Rudolf Carnap, The Value of Laws: Explanation and Prediction Carl G. Hempel, Two Basic Types of Scientific Explanation Carl G. Hempel, The Thesis of Structural Identity Carl G. Hempel, Inductive-Statistical Explanation Peter Railton, A Deductive-Nomological Model of Probabilistic Explanation *Philip Kitcher, Explanatory Unification *James Woodward, The Manipulability Conception of Causal Explanation Commentary 7 | Laws of Nature Introduction A. J. Ayer, What Is a Law of Nature? Fred I. Dretske, Laws of Nature D. H. Mellor, Necessities and Universals in Natural Laws Nancy Cartwright, Do the Laws of Physics State the Facts? Commentary 8 | Intertheoretic Reduction Introduction Ernest Nagel, Issues in the Logic of Reductive Explanations Paul K. Feyerabend, How to Be a Good Empiricist *Jerry A. Fodor, Special Sciences Philip Kitcher, 1953 and All That: A Tale of Two Sciences Commentary 9 | Empiricism and Scientific Realism Introduction Grover Maxwell, The Ontological Status of Theoretical Entities Bas C. van Fraassen, Arguments Concerning Scientific Realism Alan Musgrave, Realism versus Constructive Empiricism Larry Laudan, A Confutation of Convergent Realism *Juha T. Saatsi, On the Pessimistic Induction and Two Fallacies Ian Hacking, Experimentation and Scientific Realism David B. Resnik, Hacking’s Experimental Realism *Martin Carrier, What Is Right with the Miracle Argument Arthur Fine, The Natural Ontological Attitude Alan Musgrave, NOA’s Ark---Fine for Realism Commentary Glossary Bibliography Name Index Subject Index. (shrink)

Discussions of science and values in risk management have largely focused on how values enter into arguments about risks, that is, issues of acceptable risk. Instead this volume concentrates on how values enter into collecting, interpreting, communicating, and evaluating the evidence of risks, that is, issues of the acceptability of evidence of risk. By focusing on acceptable evidence, this volume avoids two barriers to progress. One barrier assumes that evidence of risk is largely a matter of objective scientific data and (...) therefore uncontroversial. The other assumes that evidence of risk, being "just" a matter of values, is not amenable to reasoned critique. Denying both extremes, this volume argues for a more constructive conclusion: understanding the interrelations of scientific and value issues enables a critical scrutiny of risk assessments and better public deliberation about social choices. The contributors, distinguished philosophers, policy analysts, and natural and social scientists, analyze environmental and medical controversies, and assumptions underlying views about risk assessment and the scientific and statistical models used in risk management. (shrink)

As we go to press, the threat of biological or chemical warfare in the Persian Gulf is no longer imminent. Yet the questions raised by the proposed use of “investigational drugs,” without informed consent, to protect U.S. troops remain. The article by Edmund G. Howe and Edward D. Martin presents the arguments that informed the Pentagon's thinking on the subject. It and the commentaries, by George J. Annas and Michael A. Grodin, and Robert J. Levine, explore, among others, issues (...) of informed consent, of distinguishing ‘research’ and ‘treatment,’ and of the nature and extent of the autonomy of military personnel.—B‐JC, HLN. (shrink)

In people with chronic low back pain, maladaptive structural and functional changes on a cortical level have been identified. On a functional level, somatosensory cortical excitability has been shown to be reduced in chronic pain conditions, resulting in cortical disinhibition. The occurrence of structural and/or functional maladaptive cortical changes in people with CLBP could play a role in maintaining the pain. There is currently no measurement protocol for cortical excitability that employs stimulation directly to the lower back. We developed a (...) protocol for the measurement of single pulse somatosensory evoked potential waveforms and paired-pulse behavior generated from sensory nerves of the lower back and quantified its test–retest reliability in a sample of 30 healthy individuals to gain insights into the normal variability of cortical responses, which could then be compared to results from people with CLBP. We investigated cortical excitability by measuring SEPs and PPB. PPB was defined as the ratio of the amplitude of the second cortical response divided by the first cortical response. A2s was determined by subtracting the response to single-pulse stimuli from the paired pulse stimuli response to account for linear superposition effects. The test–retest reliability of the protocol was very poor with no evidence of systematic bias but a high amount of random variability between sessions. There was no significant difference in the right side PPB for session 1 and session 2 ; mean session difference [ = −0.44 ; t = −1.17, p = 0.26]. The ICC3.1 for the outlier-removed right side PPB were 0.19 and 0.43 for left side PPB. This finding potentially has wider implications for PPB protocols. If these findings were replicated in other groups and other nerves, it would question the validity of this measure more generally. However, these findings are restricted to healthy people and sensory nerves of the lower back and may not be generalizable. (shrink)

In this work, on the one hand, we examined the relationship between emotional intelligence (EI) and empathy and, on the other, the relationship between emotional intelligence and nonverbal sensitivity, through two independent studies. The first study analyzed the relationship between dimensions of emotional intelligence and aspects of empathy, in a sample of 856 participants who completed two measures of EI, the Trait Meta-Mood Scale (TMMS) and the Trait Emotional Intelligence Questionnaire (TEIQue), and a measure of empathy, The Interpersonal Reactivity Index (...) (IRI). The results showed a similar pattern of significant positive relations in all the EI domains with empathic Perspective-taking, and significant negative relationships with empathic Personal Distress, except for the EI domain of Attention, which had a positive relationship. Stepwise multiple regression analyses showed that the dimension that best predicted empathic Perspective-taking and Empathic Concern was the Emotionality Factor; whereas Attention best predicted empathic Fantasy; and the Self-Control Factor best predicted Personal Distress, although negatively. Gender emerged as a predictor of three empathic aspects, Fantasy, Empathic Concern, and Personal Distress, with women obtaining higher scores than men in all of them. Age was the only predictor of Fantasy, with a negative relationship. The second study involved 646 people who completed the same measures of EI as the participants of the first study and the Mini-Profile of Nonverbal Sensitivity (Mini-PONS). The results showed some significant relationships between EI dimensions and the channels and quadrants of the MiniPONS. Stepwise multiple regression analyses showed that very few EI dimensions predicted nonverbal sensitivity, with Attention obtaining the best result. Both gender and age emerged as predictors, some in unique cases, of channels, quadrants and of the total score of nonverbal sensitivity; age had a negative relationship, and women obtained higher scores than men. (shrink)

The seventeenth century was a period of extraordinary invention, discovery and revolutions in scientific, social and political orders. It was a time of expansive automation, biological discovery, rapid advances in medical knowledge, of animal trials and a questioning of the boundaries between species, human and non-human, between social classes, and of the assumed naturalness of political inequality. This book gives a tour through those objects, ordinary and extraordinary, which captivated the philosophical imagination of the single most important French philosopher of (...) this period, Rene Descartes. Deborah J. Brown and Calvin G. Normore document Descartes' attempt to make sense of the complex, composite objects of human and divine invention, consistent with the fundamental tenets of his metaphysical system. Their central argument is that, far from reducing all the categories of ordinary experience to the two basic categories of substance, mind and body, Descartes' philosophy recognises irreducible composites that resist reduction, and require their own distinctive modes of explanation. (shrink)

This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.

Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities (...) is to ensure that only statistical hypotheses that have passed severe or probative tests are inferred from the data. The severity criterion supplies a meta-statistical principle for evaluating proposed statistical inferences, avoiding classic fallacies from tests that are overly sensitive, as well as those not sensitive enough to particular errors and discrepancies. Introduction and overview 1.1 Behavioristic and inferential rationales for Neyman–Pearson (N–P) tests 1.2 Severity rationale: induction as severe testing 1.3 Severity as a meta-statistical concept: three required restrictions on the N–P paradigm Error statistical tests from the severity perspective 2.1 N–P test T(): type I, II error probabilities and power 2.2 Specifying test T() using p-values Neyman's post-data use of power 3.1 Neyman: does failure to reject H warrant confirming H? Severe testing as a basic concept for an adequate post-data inference 4.1 The severity interpretation of acceptance (SIA) for test T() 4.2 The fallacy of acceptance (i.e., an insignificant difference): Ms Rosy 4.3 Severity and power Fallacy of rejection: statistical vs. substantive significance 5.1 Taking a rejection of H0 as evidence for a substantive claim or theory 5.2 A statistically significant difference from H0 may fail to indicate a substantively important magnitude 5.3 Principle for the severity interpretation of a rejection (SIR) 5.4 Comparing significant results with different sample sizes in T(): large n problem 5.5 General testing rules for T(), using the severe testing concept The severe testing concept and confidence intervals 6.1 Dualities between one and two-sided intervals and tests 6.2 Avoiding shortcomings of confidence intervals Beyond the N–P paradigm: pure significance, and misspecification tests Concluding comments: have we shown severity to be a basic concept in a N–P philosophy of induction? (shrink)

While many philosophers of science have accorded special evidential significance to tests whose results are "novel facts", there continues to be disagreement over both the definition of novelty and why it should matter. The view of novelty favored by Giere, Lakatos, Worrall and many others is that of use-novelty: An accordance between evidence e and hypothesis h provides a genuine test of h only if e is not used in h's construction. I argue that what lies behind the intuition that (...) novelty matters is the deeper intuition that severe tests matter. I set out a criterion of severity akin to the notion of a test's power in Neyman-Pearson statistics. I argue that tests which are use-novel may fail to be severe, and tests that are severe may fail to be use-novel. I discuss the 1919 eclipse data as a severe test of Einstein's law of gravity. (shrink)

I argue that the Bayesian Way of reconstructing Duhem's problem fails to advance a solution to the problem of which of a group of hypotheses ought to be rejected or "blamed" when experiment disagrees with prediction. But scientists do regularly tackle and often enough solve Duhemian problems. When they do, they employ a logic and methodology which may be called error statistics. I discuss the key properties of this approach which enable it to split off the task of testing auxiliary (...) hypotheses from that of appraising a primary hypothesis. By discriminating patterns of error, this approach can at least block, if not also severely test, attempted explanations of an anomaly. I illustrate how this approach directs progress with Duhemian problems and explains how scientists actually grapple with them. (shrink)

Although both philosophers and scientists are interested in how to obtain reliable knowledge in the face of error, there is a gap between their perspectives that has been an obstacle to progress. By means of a series of exchanges between the editors and leaders from the philosophy of science, statistics and economics, this volume offers a cumulative introduction connecting problems of traditional philosophy of science to problems of inference in statistical and empirical modelling practice. Philosophers of science and scientific practitioners (...) are challenged to reevaluate the assumptions of their own theories - philosophical or methodological. Practitioners may better appreciate the foundational issues around which their questions revolve and thereby become better 'applied philosophers'. Conversely, new avenues emerge for finally solving recalcitrant philosophical problems of induction, explanation and theory testing. (shrink)

In the conclusion to this multi-part article I first review the discussions carried out around the six essential questions in psychiatric diagnosis – the position taken by Allen Frances on each question, the commentaries on the respective question along with Frances’ responses to the commentaries, and my own view of the multiple discussions. In this review I emphasize that the core question is the first – what is the nature of psychiatric illness – and that in some manner all further (...) questions follow from the first. Following this review I attempt to move the discussion forward, addressing the first question from the perspectives of natural kind analysis and complexity analysis. This reflection leads toward a view of psychiatric disorders – and future nosologies – as far more complex and uncertain than we have imagined. (shrink)

The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actually met in the studies available, e.g., in psychology, ecology, biology, risk assessment. Philosophical scrutiny can help disentangle 'practical' problems of model validation, and conversely, (...) a methodology of statistical model validation can shed light on a number of issues of interest to philosophers of science. (shrink)

The key problem in the controversy over group selection is that of defining a criterion of group selection that identifies a distinct causal process that is irreducible to the causal process of individual selection. We aim to clarify this problem and to formulate an adequate model of irreducible group selection. We distinguish two types of group selection models, labeling them type I and type II models. Type I models are invoked to explain differences among groups in their respective rates of (...) production of contained individuals. Type II models are invoked to explain differences among groups in their respective rates of production of distinct new groups. Taking Elliott Sober's model as an exemplar, we argue that although type I models have some biological importance--they force biologists to consider the role of group properties in influencing the fitness of organisms--they fail to identify a distinct group-level causal selection process. Type II models if properly framed, however, do identify a group-level causal selection process that is not reducible to individual selection. We propose such a type II model and apply it to some of the major candidates for group selection. (shrink)

Kuhn maintains that what marks the transition to a science is the ability to carry out ‘normal’ science—a practice he characterizes as abandoning the kind of testing that Popper lauds as the hallmark of science. Examining Kuhn's own contrast with Popper, I propose to recast Kuhnian normal science. Thus recast, it is seen to consist of severe and reliable tests of low-level experimental hypotheses (normal tests) and is, indeed, the place to look to demarcate science. While thereby vindicating Kuhn on (...) demarcation, my recasting of normal science is seen to tell against Kuhn's view of revolutionary science. (shrink)

In seeking general accounts of evidence, confirmation, or inference, philosophers have looked to logical relationships between evidence and hypotheses. Such logics of evidential relationship, whether hypothetico-deductive, Bayesian, or instantiationist fail to capture or be relevant to scientific practice. They require information that scientists do not generally have (e.g., an exhaustive set of hypotheses), while lacking slots within which to include considerations to which scientists regularly appeal (e.g., error probabilities). Building on my co-symposiasts contributions, I suggest some directions in which a (...) new and more adequate philosophy of evidence can move. (shrink)

Theories of statistical testing may be seen as attempts to provide systematic means for evaluating scientific conjectures on the basis of incomplete or inaccurate observational data. The Neyman-Pearson Theory of Testing (NPT) has purported to provide an objective means for testing statistical hypotheses corresponding to scientific claims. Despite their widespread use in science, methods of NPT have themselves been accused of failing to be objective; and the purported objectivity of scientific claims based upon NPT has been called into question. The (...) purpose of this paper is first to clarify this question by examining the conceptions of (I) the function served by NPT in science, and (II) the requirements of an objective theory of statistics upon which attacks on NPT's objectivity are based. Our grounds for rejecting these conceptions suggest altered conceptions of (I) and (II) that might avoid such attacks. Second, we propose a reformulation of NPT, denoted by NPT*, based on these altered conceptions, and argue that it provides an objective theory of statistics. The crux of our argument is that by being able to objectively control error frequencies NPT* is able to objectively evaluate what has or has not been learned from the result of a statistical test. (shrink)

While orthodox (Neyman-Pearson) statistical tests enjoy widespread use in science, the philosophical controversy over their appropriateness for obtaining scientific knowledge remains unresolved. I shall suggest an explanation and a resolution of this controversy. The source of the controversy, I argue, is that orthodox tests are typically interpreted as rules for making optimal decisions as to how to behave--where optimality is measured by the frequency of errors the test would commit in a long series of trials. Most philosophers of statistics, however, (...) view the task of statistical methods as providing appropriate measures of the evidential-strength that data affords hypotheses. Since tests appropriate for the behavioral-decision task fail to provide measures of evidential-strength, philosophers of statistics claim the use of orthodox tests in science is misleading and unjustified. What critics of orthodox tests overlook, I argue, is that the primary function of statistical tests in science is neither to decide how to behave nor to assign measures of evidential strength to hypotheses. Rather, tests provide a tool for using incomplete data to learn about the process that generated it. This they do, I show, by providing a standard for distinguishing differences (between observed and hypothesized results) due to accidental or trivial errors from those due to systematic or substantively important discrepancies. I propose a reinterpretation of a commonly used orthodox test to make this learning model of tests explicit. (shrink)

Responsible Robotics is about developing robots in ways that take their social implications into account, which includes conceptually framing robots and their role in the world accurately. We are now in the process of incorporating robots into our world and we are trying to figure out what to make of them and where to put them in our conceptual, physical, economic, legal, emotional and moral world. How humans think about robots, especially humanoid social robots, which elicit complex and sometimes disconcerting (...) reactions, is not predetermined. The animal–robot analogy is one of the most commonly used in attempting to frame interactions between humans and robots and it also tends to push in the direction of blurring the distinction between humans and machines. We argue that, despite some shared characteristics, when it comes to thinking about the moral status of humanoid robots, legal liability, and the impact of treatment of humanoid robots on how humans treat one another, analogies with animals are misleading. (shrink)

A critically important ethical issue facing the AI research community is how AI research and AI products can be responsibly conceptualised and presented to the public. A good deal of fear and concern about uncontrollable AI is now being displayed in public discourse. Public understanding of AI is being shaped in a way that may ultimately impede AI research. The public discourse as well as discourse among AI researchers leads to at least two problems: a confusion about the notion of (...) ‘autonomy’ that induces people to attribute to machines something comparable to human autonomy, and a ‘sociotechnical blindness’ that hides the essential role played by humans at every stage of the design and deployment of an AI system. Here our purpose is to develop and use a language with the aim to reframe the discourse in AI and shed light on the real issues in the discipline. (shrink)

A toast is due to one who slays Misguided followers of Bayes, And in their heart strikes fear and terror With probabilities of error! (E.L. Lehmann).

I document some of the main evidence showing that E. S. Pearson rejected the key features of the behavioral-decision philosophy that became associated with the Neyman-Pearson Theory of statistics (NPT). I argue that NPT principles arose not out of behavioral aims, where the concern is solely with behaving correctly sufficiently often in some long run, but out of the epistemological aim of learning about causes of experimental results (e.g., distinguishing genuine from spurious effects). The view Pearson did hold gives a (...) deeper understanding of NPT tests than their typical formulation as accept-reject routines, against which criticisms of NPT are really directed. The Pearsonian view that emerges suggests how NPT tests may avoid these criticisms while still retaining what is central to these methods: the control of error probabilities. (shrink)

Floridi and Sanders, seminal work, “On the morality of artificial agents” has catalyzed attention around the moral status of computer systems that perform tasks for humans, effectively acting as “artificial agents.” Floridi and Sanders argue that the class of entities considered moral agents can be expanded to include computers if we adopt the appropriate level of abstraction. In this paper we argue that the move to distinguish levels of abstraction is far from decisive on this issue. We also argue that (...) adopting certain levels of abstraction out of context can be dangerous when the level of abstraction obscures the humans who constitute computer systems. We arrive at this critique of Floridi and Sanders by examining the debate over the moral status of computer systems using the notion of interpretive flexibility. We frame the debate as a struggle over the meaning and significance of computer systems that behave independently, and not as a debate about the ‘true’ status of autonomous systems. Our analysis leads to the conclusion that while levels of abstraction are useful for particular purposes, when it comes to agency and responsibility, computer systems should be conceptualized and identified in ways that keep them tethered to the humans who create and deploy them. (shrink)

In this paper, we focus attention on the role of computer system complexity in ascribing responsibility. We begin by introducing the notion of technological moral action (TMA). TMA is carried out by the combination of a computer system user, a system designer (developers, programmers, and testers), and a computer system (hardware and software). We discuss three sometimes overlapping types of responsibility: causal responsibility, moral responsibility, and role responsibility. Our analysis is informed by the well-known accounts provided by Hart and Hart (...) and Honoré. While these accounts are helpful, they have misled philosophers and others by presupposing that responsibility can be ascribed in all cases of action simply by paying attention to the free and intended actions of human beings. Such accounts neglect the part played by technology in ascriptions of responsibility in cases of moral action with technology. For both moral and role responsibility, we argue that ascriptions of both causal and role responsibility depend on seeing action as complex in the sense described by TMA. We conclude by showing how our analysis enriches moral discourse about responsibility for TMA. (shrink)

After some general remarks about the interrelation between philosophical and statistical thinking, the discussion centres largely on significance tests. These are defined as the calculation of p-values rather than as formal procedures for ‘acceptance‘ and ‘rejection‘. A number of types of null hypothesis are described and a principle for evidential interpretation set out governing the implications of p- values in the specific circumstances of each application, as contrasted with a long-run interpretation. A number of more complicated situ- ations are discussed (...) in which modification of the simple p-value may be essential. (shrink)

Background: Genomic analysis may reveal both primary and secondary findings with direct relevance to the health of probands’ biological relatives. Researchers question their obligations to return findings not only to participants but also to family members. Given the social value of privacy protection, should researchers offer a proband’s results to family members, including after the proband’s death? Methods: Preferences were elicited using interviews and a survey. Respondents included probands from two pancreatic cancer research resources, plus biological and nonbiological family members. (...) Hypothetical scenarios based on actual research findings from the two cancer research resources were presented; participants were asked return of results preferences and justifications. Interview transcripts were coded and analyzed; survey data were analyzed descriptively. Results: Fifty-one individuals (17 probands, 21 biological relatives, 13 spouses/partners) were interviewed. Subsequently, a mailed survey was returned by 464 probands, 1,040 biological family members, and 399 spouses/partners. This analysis highlights the interviews, augmented by survey findings. Probands and family members attribute great predictive power and lifesaving potential to genomic information. A majority hold that a proband’s genomic results relevant to family members’ health ought to be offered. While informants endorse each individual’s choice whether to learn results, most express a strong moral responsibility to know and to share, particularly with the younger generation. Most have few concerns about sharing genetic information within the family; rather, their concerns focus on the health consequences of not sharing. Conclusions: Although additional studies in diverse populations are needed, policies governing return of genomic results should consider how families understand genomic data, how they value confidentiality within the family, and whether they endorse an ethics of sharing. A focus on respect for individual privacy—without attention to how the broad social and cultural context shapes preferences within families—cannot be the sole foundation of policy. (shrink)

The concept of agency as applied to technological artifacts has become an object of heated debate in the context of AI research because some AI researchers ascribe to programs the type of agency traditionally associated with humans. Confusion about agency is at the root of misconceptions about the possibilities for future AI. We introduce the concept of a triadic agency that includes the causal agency of artifacts and the intentional agency of humans to better describe what happens in AI as (...) it functions in real-world contexts. We use the VW emission fraud case to explain triadic agency since in this case a technological artifact, namely software, was an essential part of the wrongdoing and the software might be said to have agency in the wrongdoing. We then extend the case to include futuristic AI, imagining AI that becomes more and more autonomous. (shrink)

Book description: The capacity to represent and think about time is one of the most fundamental and least understood aspects of human cognition and consciousness. This book throws new light on central issues in the study of the mind by uniting, for the first time, psychological and philosophical approaches dealing with the connection between temporal representation and memory. Fifteen specially written essays by leading psychologists and philosophers investigate the way in which time is represented in memory, and the role memory (...) plays in our ability to reason about time. They offer insights into current theories of memory processes and of the mechanisms and cognitive abilities underlying temporal judgements, and draw out fundamental issues concerning the phenomenology and epistemology of memory and our understanding of time. The chapters are arranged into four sections, each focused on one area of current research: Keeping Track of Time, and Temporal Representation; Memory, Awareness and the Past; Memory and Experience; Knowledge and the Past: The Epistemology and Metaphysics of Time. A general introduction gives an overview of the topics discussed and makes explicit central themes which unify the different philosophical and psychological approaches. (shrink)

We argue that a responsible analysis of today's evidence-based risk assessments and risk debates in biology demands a critical or metascientific scrutiny of the uncertainties, assumptions, and threats of error along the manifold steps in risk analysis. Without an accompanying methodological critique, neither sensitivity to social and ethical values, nor conceptual clarification alone, suffices. In this view, restricting the invitation for philosophical involvement to those wearing a "bioethicist" label precludes the vitally important role philosophers of science may be able to (...) play as bioevidentialists. The goal of this paper is to give a brief and partial sketch of how a metascientific scrutiny of risk evidence might work. (shrink)

An important theme to have emerged from the new experimentalist movement is that much of actual scientific practice deals not with appraising full-blown theories but with the manifold local tasks required to arrive at data, distinguish fact from artifact, and estimate backgrounds. Still, no program for working out a philosophy of experiment based on this recognition has been demarcated. I suggest why the new experimentalism has come up short, and propose a remedy appealing to the practice of standard error statistics. (...) I illustrate a portion of my proposal using Galison's experimental narrative on neutral currents. (shrink)

Scholarship on embedding values in AI is growing. In what follows, we distinguish two concepts of AI and argue that neither is amenable to values being ‘embedded’. If we think of AI as computational artifacts, then values and AI cannot be added together because they are ontologically distinct. If we think of AI as sociotechnical systems, then components of values and AI are in the same ontologic category—they are both social. However, even here thinking about the relationship as one of (...) ‘embedding’ is a mischaracterization. The relationship between values and AI is best understood as a dimension of the relationship between technology and society, a relationship that can be theorized in multiple ways. The literature in this area is consistent in showing that technology and society are co-productive. Within the co-production framework, the relationship between values and AI is shown to be generative of new meaning. This stands in stark contrast to the framework of ‘embedding’ values which frames values as fixed things that can be inserted into technological artifacts. (shrink)

The issues of double-counting, use-constructing, and selection effects have long been the subject of debate in the philosophical as well as statistical literature. I have argued that it is the severity, stringency, or probativeness of the test—or lack of it—that should determine if a double-use of data is admissible. Hitchcock and Sober ([2004]) question whether this ‘severity criterion' can perform its intended job. I argue that their criticisms stem from a flawed interpretation of the severity criterion. Taking their criticism as (...) a springboard, I elucidate some of the central examples that have long been controversial, and clarify how the severity criterion is properly applied to them. Severity and Use-Constructing: Four Points (and Some Clarificatory Notes) 1.1 Point 1: Getting beyond ‘all or nothing’ standpoints 1.2 Point 2: The rationale for prohibiting double-counting is the requirement that tests be severe 1.3 Point 3: Evaluate severity of a test T by its associated construction rule R 1.4 Point 4: The ease of passing vs. ease of erroneous passing: Statistical vs. ‘Definitional’ probability The False Dilemma: Hitchcock and Sober 2.1 Marsha measures her desk reliably 2.2 A false dilemma Canonical Errors of Inference 3.1 How construction rules may alter the error-probing performance of tests 3.2 Rules for accounting for anomalies 3.3 Hunting for statistically significant differences Concluding Remarks. (shrink)