In this book Bruno Latour brings together these different approaches to provide a lively and challenging analysis of science, demonstrating how social context..

This is a welcome reprint of a book that continues to grow in importance.

I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to (...) the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?". (shrink)

Chapter 1 FROM ORDER TO DISORDER 5 mins. John enters and goes into his office. He says something very quickly about having made a bad mistake. He had sent the review of a paper. . . . The rest of the sentence is inaudible. 5 mins.

In a new introduction, the authors describe how science and its social context were understood when this book was first published, and how the study of the history of science has changed since then.

Our trust in the word of others is often dismissed as unworthy, because the illusory ideal of "autonomous knowledge" has prevailed in the debate about the nature of knowledge. Yet we are profoundly dependent on others for a vast amount of what any of us claim to know. Coady explores the nature of testimony in order to show how it might be justified as a source of knowledge, and uses the insights that he has developed to challenge certain widespread assumptions (...) in the areas of history, law, mathematics, and psychology. (shrink)

Artificial intelligence (AI) and robotics are digital technologies that will have significant impact on the development of humanity in the near future. They have raised fundamental questions about what we should do with these systems, what the systems themselves should do, what risks they involve, and how we can control these. - After the Introduction to the field (§1), the main themes (§2) of this article are: Ethical issues that arise with AI systems as objects, i.e., tools made and used (...) by humans. This includes issues of privacy (§2.1) and manipulation (§2.2), opacity (§2.3) and bias (§2.4), human-robot interaction (§2.5), employment (§2.6), and the effects of autonomy (§2.7). Then AI systems as subjects, i.e., ethics for the AI systems themselves in machine ethics (§2.8) and artificial moral agency (§2.9). Finally, the problem of a possible future AI superintelligence leading to a “singularity” (§2.10). We close with a remark on the vision of AI (§3). - For each section within these themes, we provide a general explanation of the ethical issues, outline existing positions and arguments, then analyse how these play out with current technologies and finally, what policy consequences may be drawn. (shrink)

In A Social History of Truth, Shapin engages these universal questions through an elegant recreation of a crucial period in the history of early modern science: ...

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate or state (...) secrecy, opacity as technical illiteracy, and an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. (shrink)

Paul Faulkner presents a new theory of testimony - the basis of much of what we know. He addresses the questions of what makes it reasonable to accept a piece of testimony, and what warrants belief formed on that basis. He rejects rival theories and argues that testimonial knowledge and testimonially warranted belief are based on trust.

Artificial agents (AAs), particularly but not only those in Cyberspace, extend the class of entities that can be involved in moral situations. For they can be conceived of as moral patients (as entities that can be acted upon for good or evil) and also as moral agents (as entities that can perform actions, again for good or evil). In this paper, we clarify the concept of agent and go on to separate the concerns of morality and responsibility of agents (most (...) interestingly for us, of AAs). We conclude that there is substantial and important scope, particularly in Computer Ethics, for the concept of moral agent not necessarily exhibiting free will, mental states or responsibility. This complements the more traditional approach, common at least since Montaigne and Descartes, which considers whether or not (artificial) agents have mental states, feelings, emotions and so on. By focussing directly on mind-less morality we are able to avoid that question and also many of the concerns of Artificial Intelligence. A vital component in our approach is the Method of Abstraction for analysing the level of abstraction (LoA) at which an agent is considered to act. The LoA is determined by the way in which one chooses to describe, analyse and discuss a system and its context. The Method of Abstraction is explained in terms of an interface or set of features or observables at a given LoA. Agenthood, and in particular moral agenthood, depends on a LoA. Our guidelines for agenthood are: interactivity (response to stimulus by change of state), autonomy (ability to change state without stimulus) and adaptability (ability to change the transition rules by which state is changed) at a given LoA. Morality may be thought of as a threshold defined on the observables in the interface determining the LoA under consideration. An agent is morally good if its actions all respect that threshold; and it is morally evil if some action violates it. That view is particularly informative when the agent constitutes a software or digital system, and the observables are numerical. Finally we review the consequences for Computer Ethics of our approach. In conclusion, this approach facilitates the discussion of the morality of agents not only in Cyberspace but also in the biosphere, where animals can be considered moral agents without their having to display free will, emotions or mental states, and in social contexts, where systems like organizations can play the role of moral agents. The primary cost of this facility is the extension of the class of agents and moral agents to embrace AAs. (shrink)

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI (...) systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice. (shrink)

Martin Kusch puts forth two controversial ideas: that knowledge is a social status and that knowledge is primarily the possession of groups rather than individuals. He defends the radical implications of his views: that knowledge is political, and that it varies with communities. This bold approach to epistemology is a challenge to philosophy and the wider academic world.

If a robot sends a deceptive signal to a human user, is this always and everywhere an unethical act, or might it sometimes be ethically desirable? Building upon previous work in robot ethics, this article tries to clarify and refine our understanding of the ethics of robotic deception. It does so by making three arguments. First, it argues that we need to distinguish between three main forms of robotic deception (external state deception; superficial state deception; and hidden state deception) in (...) order to think clearly about its ethics. Second, it argues that the second type of deception – superficial state deception – is not best thought of as a form of deception, even though it is frequently criticised as such. And third, it argues that the third type of deception is best understood as a form of betrayal because doing so captures the unique ethical harm to which it gives rise, and justifies special ethical protections against its use. (shrink)

People increasingly form beliefs based on information gained from automatically filtered Internet ‎sources such as search engines. However, the workings of such sources are often opaque, preventing ‎subjects from knowing whether the information provided is biased or incomplete. Users’ reliance on ‎Internet technologies whose modes of operation are concealed from them raises serious concerns about ‎the justificatory status of the beliefs they end up forming. Yet it is unclear how to address these concerns ‎within standard theories of knowledge and justification. (...) To shed light on the problem, we introduce a ‎novel conceptual framework that clarifies the relations between justified belief, epistemic responsibility, ‎action, and the technological resources available to a subject. We argue that justified belief is subject to ‎certain epistemic responsibilities that accompany the subject’s particular decision-taking circumstances, ‎and that one typical responsibility is to ascertain, so far as one can, whether the information upon which ‎the judgment will rest is biased or incomplete. What this responsibility comprises is partly determined by ‎the inquiry-enabling technologies available to the subject. We argue that a subject’s beliefs that are ‎formed based on Internet-filtered information are less justified than they would be if she either knew how ‎filtering worked or relied on additional sources, and that the subject may have the epistemic ‎responsibility to take measures to enhance the justificatory status of such beliefs.‎. (shrink)

We often talk about knowledge being transmitted via testimony. This suggests a picture of testimony with striking similarities to memory. For instance, it is often assumed that neither is a generative source of knowledge: while the former transmits knowledge from one speaker to another, the latter preserves beliefs from one time to another. These considerations give rise to a stronger and a weaker thesis regarding the transmission of testimonial knowledge. The stronger thesis is that each speaker in a chain of (...) testimonial transmission must know that p in order to pass this knowledge to a hearer. The weaker thesis is that at least the first speaker must know that p in order for any hearer in the chain to come to know that p via testimony. I argue that both theses are false, and hence testimony, unlike memory, can be a generative source of knowledge. (shrink)

This paper proposes a methodological redirection of the philosophical debate on artificial moral agency in view of increasingly pressing practical needs due to technological development. This “normative approach” suggests abandoning theoretical discussions about what conditions may hold for moral agency and to what extent these may be met by artificial entities such as AI systems and robots. Instead, the debate should focus on how and to what extent such entities should be included in human practices normally assuming moral agency and (...) responsibility of participants. The proposal is backed up by an analysis of the AMA debate, which is found to be overly caught in the opposition between so-called standard and functionalist conceptions of moral agency, conceptually confused and practically inert. Additionally, we outline some main themes of research in need of attention in light of the suggested normative approach to AMA. (shrink)

The first book since Coady's 1992 'Testimony: A Philosophical Study' to offer a thorough survey and a philosophical introduction to testimony and its epistemological problems, while at the same time advancing a novel view that proposes independent justificatory pathways for the acceptance and rejection of testimony, respectively. // Table of Contents: // Introduction / 1. What is Testimony? / 2. The Testimonial Conundrum / 3. Testimony, Perception, Memory, and Inference / 4. Testimony and Evidence / 5. Reductionism and Anti-Reductionism / (...) 6. Hybrid Theories of Testimony / 7. Testimonial Knowledge: Transmission and Generation / 8. Trust and Assurance / 9. Expert Testimony / 10. Pathologies of Testimony / 11. Testimony and the Value of Knowledge / Glossary / Bibliography / Index. (shrink)

In this paper I argue that Artificial Intelligence and the many data science methods associated with it, such as machine learning and large language models, are first and foremost epistemic technologies. In order to establish this claim, I first argue that epistemic technologies can be conceptually and practically distinguished from other technologies in virtue of what they are designed for, what they do and how they do it. I then proceed to show that unlike other kinds of technology (_including_ other (...) epistemic technologies) AI can be uniquely positioned as an epistemic technology in that it is primarily designed, developed and deployed to be used in _epistemic contexts_ such as inquiry, it is specifically designed, developed and deployed to manipulate _epistemic content_ such as data, _and_ it is designed, developed and deployed to do so particularly through _epistemic operations_ such as prediction and analysis. As has been shown in recent work in the philosophy and ethics of AI (Alvarado, AI and Ethics, 2022a), understanding AI as an epistemic technology will also have significant implications for important debates regarding our relationship to AI technologies. This paper includes a brief overview of such implications, particularly those pertaining to explainability, opacity, trust and even epistemic harms related to AI technologies. (shrink)

There is a widely accepted family of views in the epistemology of testimony centering around the claim that belief is the central item involved in a testimonial exchange. For instance, in describing the process of learning via testimony, Elizabeth Fricker provides the following: “one language-user has a belief, which gives rise to an utterance by him; as a result of observing this utterance another user of the same language, his audience, comes to share that belief.” In a similar spirit, Alvin (...) Plantinga says that “…a belief on the part of the testifiee has warrant only if that belief has warrant for the testifier.” In both of these passages, we find strands of what I shall call the Belief View of Testimony. (shrink)

While much of our knowledge relies on testimony or the words of others, until recently few philosophers had much to say about the nature of testimony or how we learn from another's words, but testimony has now become a popular topic. Jennifer Lackey's Learning from Words: Testimony as a Source of Knowledge is a useful and intelligent guide, a well informed and appreciative but critical and provocative commentary on a large and growing body of literature.According to Lackey, most of the (...) literature assumes that testimony can spread but not create knowledge, much as memory can be a reminder of old but not a source of new truth. Lackey maintains that the assumption is mistaken and offers an account of testimony, according to which, testimony can give rise to new knowledge as well as transmit old truths from one person to another.In her introduction, Lackey characterizes what she calls the belief view of testimony and suggests that this view dominates today's literature. On the belief view, testimony is a vehicle for expressing belief, and when I tell you that p, I express my belief that p with the intention …. (shrink)

There is an existing debate regarding the view that technological instruments, devices, or machines can assert ‎or testify. A standard view in epistemology is that only humans can testify. However, the notion of quasi-‎testimony acknowledges that technological devices can assert or testify under some conditions, without ‎denying that humans and machines are not the same. Indeed, there are four relevant differences between ‎humans and instruments. First, unlike humans, machine assertion is not imaginative or playful. Second, ‎machine assertion is prescripted and (...) context restricted. As such, computers currently cannot easily switch ‎contexts or make meaningful relevant assertions in contexts for which they were not programmed. Third, ‎while both humans and computers make errors, they do so in different ways. Computers are very sensitive to ‎small errors in input, which may cause them to make big errors in output. Moreover, automatic error control ‎is based on finding irregularities in data without trying to establish whether they make sense. Fourth, ‎testimony is produced by a human with moral worth, while quasi-testimony is not. Ultimately, the notion of ‎quasi-testimony can serve as a bridge between different philosophical fields that deal with instruments and ‎testimony as sources of knowledge, allowing them to converse and agree on a shared description of reality, ‎while maintaining their distinct conceptions and ontological commitments about knowledge, humans, and ‎nonhumans.‎. (shrink)

Speaking with conversational AIs, technologies whose interfaces enable human-like interaction based on natural language, has become a common phenomenon. During these interactions, people form their beliefs due to the say-so of conversational AIs. In this paper, I consider, and then reject, the concepts of testimony-based beliefs and instrument-based beliefs as suitable for analysis of beliefs acquired from these technologies. I argue that the concept of instrument-based beliefs acknowledges the non-human agency of the source of the belief. However, the analysis focuses (...) on perceiving signs and indicators rather than content expressed in natural language. At the same time, the concept of testimony-based beliefs does refer to natural language propositions, but there is an underlying assumption that the agency of the testifier is human. To fill the lacuna of analyzing belief acquisition from conversational AIs, I suggest a third concept: technology-based beliefs. It acknowledges the non-human agency-status of the originator of the belief. Concurrently, the focus of analysis is on the propositional content that forms the belief. Filling the lacuna enables analysis that considers epistemic, ethical, and social issues of conversational AIs without excluding propositional content or compromising accepted assumptions about the agency of technologies. (shrink)

Philosophical perplexity about intention begins with its appearance in three guises: intention for the future, as when I intend to complete this entry by the end of the month; the intention with which someone acts, as I am typing with the further intention of writing an introductory sentence; and intentional action, as in the fact that I am typing these words intentionally. As Elizabeth Anscombe wrote in a similar context, ‘it is implausible to say that the word is equivocal as (...) it occurs in these different cases’ and from the fact that ‘we are tempted to speak of “different senses” of a word which is clearly not equivocal, we may infer that we are pretty much in the dark about the character of the concept which it represents’ (Anscombe 1963, p. 1). (shrink)

Testimony is indispensable in the sciences. To deny the propriety of relying on it engenders an untenable scepticism. But this leaves open the issue of what exactly confers a scientist’s epistemic right to rely upon the word of her colleagues. Some authors have suggested a recipient of testimony enjoys an epistemic entitlement to trust the word of another as such, not requiring evidence of her trustworthiness, so long as there is not evidence of her untrustworthiness. I argue that, whether or (...) not such an on-no-evidence entitlement to believe what one is told exists, it shrinks to irrelevance in the explanation of the basis on which scientists take each other’s word in the scientific community. This is so, since a normally knowledgeable adult hearer is typically awash with relevant evidence, direct and circumstantial, for and against, concerning a teller’s trustworthiness, and this swamps any alleged entitlement to believe in the absence of such evidence. There need not be personal knowledge of the teller, since social role and topic provide evidence regarding trustworthiness. I also discuss the individuation of ‘testimony’ as an epistemic kind. I suggest that we should not attempt to define a category with sharp boundaries, but instead characterise a paradigm case—one person telling another something in face-to-face personal communication—and then notice other cases which both resemble and diverge from this in epistemically relevant features—lectures, media broadcasts, personal letters, personal diaries, etc.Author Keywords: Testimony; Justification; Knowledge; Individualism; Communication. (shrink)

Can we trust robots? Responding to the literature on trust and e-trust, this paper asks if the question of trust is applicable to robots, discusses different approaches to trust, and analyses some preconditions for trust. In the course of the paper a phenomenological-social approach to trust is articulated, which provides a way of thinking about trust that puts less emphasis on individual choice and control than the contractarian-individualist approach. In addition, the argument is made that while robots are neither human (...) nor mere tools, we have sufficient functional, agency-based, appearance-based, social-relational, and existential criteria left to evaluate trust in robots. It is also argued that such evaluations must be sensitive to cultural differences, which impact on how we interpret the criteria and how we think of trust in robots. Finally, it is suggested that when it comes to shaping conditions under which humans can trust robots, fine-tuning human expectations and robotic appearances is advisable. (shrink)

Trust is one of the big buzzwords in debates about the shaping of society, democracy, and emerging technologies. For example, one prominent idea put forward by the High‐Level Expert Group on Artificial Intelligence appointed by the European Commission is that artificial intelligence should be trustworthy. In this essay, we explore the notion of trust and argue that both proponents and critics of trustworthy AI have flawed pictures of the nature of trust. We develop an approach to understanding trust in AI (...) that does not conceive of trust merely as an accelerator for societal acceptance of AI technologies. Instead, we argue, trust is granted through leaps of faith. For this reason, trust remains precarious, fragile, and resistant to promotion through formulaic approaches. We also highlight the significance of distrust in societal deliberation, as it is relevant to trust in various and intricate ways. Among the fruitful aspects of distrust is that it enables individuals to forgo technology if desired, to constrain its power, and to exercise meaningful human control. (shrink)

In other work I have defended the claim that, when we rely on other speakers by accepting what they tell us, our reliance on them differs in epistemically relevant ways from our reliance on instruments, when we rely on them by accepting what they “tell” us. However, where I have explored the former sort of reliance at great length, I have not done so with the latter. In this paper my aim is to do so. My key notions will be (...) those of our social practices, the normative expectations that are sanctioned by those practices, and the epistemically engineered environments constituted by some of these practices. With these notions in mind, I will argue that one’s reliance on instruments, while relevantly different from one’s reliance on other speakers, can nevertheless manifest a kind of epistemic dependence which epistemological theory can and should acknowledge. (shrink)

This essay warns of eroding accountability in computerized societies. It argues that assumptions about computing and features of situations in which computers are produced create barriers to accountability. Drawing on philosophical analyses of moral blame and responsibility, four barriers are identified: 1) the problem of many hands, 2) the problem of bugs, 3) blaming the computer, and 4) software ownership without liability. The paper concludes with ideas on how to reverse this trend.

Let us focus on what I take it is the paradigm case of testimony—the intentional transfer of a belief from one agent to another, whether in the usual way via a verbal assertion made by the one agent to the other, or by some other means, such as through a note.1 So, for example, John says to Mary that the house is on fire (or, if you like, ‘texts’ her this message on her phone), and Mary, upon hearing this, forms (...) the belief that the house is on fire and consequently exits the building at speed. Clearly, a great deal of our beliefs are gained via testimony, and if the epistemic status of our testimonybased beliefs were to be called into question en masse, then this would present us with quite a predicament. It is thus essential that we have some plausible account of the epistemology of testimony. Our primary focus will be on the justification for our testimony-based beliefs, though along the way we will say a little about other relevant epistemic notions like epistemic entitlement as well. (shrink)

Technology is a practically indispensible means for satisfying one’s basic interests in all central areas of human life including nutrition, habitation, health care, entertainment, transportation, and social interaction. It is impossible for any one person, even a well-trained scientist or engineer, to know enough about how technology works in these different areas to make a calculated choice about whether to rely on the vast majority of the technologies she/he in fact relies upon. Yet, there are substantial risks, uncertainties, and unforeseen (...) practical consequences associated with the use of technological artifacts and systems. The salience of technological failure (both catastrophic and mundane), as well as technology’s sometimes unforeseeable influence on our behavior, makes it relevant to wonder whether we are really justified as individuals in our practical reliance on technology. Of course, even if we are not justified, we might nonetheless continue in our technological reliance, since the alternatives might not be attractive or feasible. In this chapter I argue that a conception of trust in technological artifacts and systems is plausible and helps us understand what is at stake philosophically in our reliance on technology. Such an account also helps us understand the relationship between trust and technological risk and the ethical obligations of those who design, manufacture, and deploy technological artifacts. (shrink)

C.A.J. Coady, in his book Testimony: A Philosophical Study (Oxford: Clarendon Press, 1992), offers conditions on an assertion that p to count as testimony. He claims that the assertion that p must be by a competent speaker directed to an audience in need of evidence and it must be evidence that p. I offer examples to show that Coady’s conditions are too strong. Testimony need not be evidence; the speaker need not be competent; and, the statement need not be relevant (...) or directed to someone in need of evidence. I give alternative conditions. Coady was led into the stronger conditions by investigating testimony as it occurs in legal contexts, where special steps are taken to ensure that testimony provides the jury or the judge with evidence by a competent speaker that is relevant to the disputed question of the guilt or innocence of the defendant. (shrink)

There remains no consensus among social scientists as to how to measure and understand forms of information deprivation such as misinformation. Machine learning and statistical analyses of information deprivation typically contain problematic operationalizations which are too often biased towards epistemic elites' conceptions that can undermine their empirical adequacy. A mature science of information deprivation should include considerable citizen involvement that is sensitive to the value-ladenness of information quality and that doing so may improve the predictive and explanatory power of extant (...) models. (shrink)

In Relying on others [Goldberg, S. 2010a. Relying on others: An essay in epistemology. Oxford: Oxford University Press], I argued that, from the perspective of an interest in epistemic assessment, the testimonial belief-forming process should be regarded as interpersonally extended. At the same time, I explicitly rejected the extendedness model for beliefs formed through reliance on a mere mechanism, such as a clock. In this paper, I try to bolster my defense of this asymmetric treatment. I argue that a crucial (...) assumption lying behind the argument I used to establish interpersonal extendedness in testimony cases does not apply to beliefs formed through reliance on instruments. In this respect, at least, there appears to be something epistemically distinctive about relying on another epistemic agent. (shrink)

We are becoming increasingly dependent on robots and other forms of artificial intelligence for our beliefs. But how should the knowledge gained from the “say-so” of a robot be classified? Should it be understood as testimonial knowledge, similar to knowledge gained in conversation with another person? Or should it be understood as a form of instrument-based knowledge, such as that gained from a calculator or a sundial? There is more at stake here than terminology, for how we treat objects as (...) sources of knowledge often has important social and legal consequences. In this paper, I argue that at least some robots are capable of testimony. I make my argument by exploring the differences between instruments and testifiers on a well-known account of knowledge: reliabilism. On this approach, I claim that the difference between instruments and testifiers as sources of knowledge is that only the latter are capable of deception. As some robots can be designed to deceive, so they too should be recognized as testimonial sources of knowledge. (shrink)

Philosophers agree that an important part of our knowledge is acquired via testimony. One of the main objectives of social epistemology is therefore to specify the conditions under which a hearer is justified in accepting a proposition stated by a source. Non-reductionists, who think that testimony could be considered as an a priori source of knowledge, as well as reductionists, who think that another type of justification has to be added to testimony, share a common conception about children development. Non-reductionists (...) believe that infants and children are fundamentally gullible and their gullibility could be seen as an example for justifying testimony, while reductionists believe that this gullibility is merely an exception that should be taken into account. The objective of this paper is to review contemporary literature in developmental psychology providing empirical grounds likely to clarify this philosophical debate. What emerges from current research is a more elaborated vision of children’s attitude toward testimony. Even at a very young age, children do not blindly swallow information coming from testimony; doubtful or contradictory information is automatically screened by their cognitive system. Even if they are unable to give positive reasons for the acceptance of a given testimony, young children are not gullible. Such empirical findings tend to call into question the radical opposition between reductionism and non-reductionism. (shrink)

Given the complexity of teams involved in creating AI-based systems, how can we understand who should be held accountable when they fail? This paper reports findings about accountable AI from 26 interviews conducted with stakeholders in AI drawn from the fields of AI research, law, and policy. Participants described the challenges presented by the distributed nature of how AI systems are designed, developed, deployed, and regulated. This distribution of agency, alongside existing mechanisms of accountability, responsibility, and liability, creates barriers for (...) effective accountable design. As agency is distributed across the socio-technical landscape of an AI system, users without deep knowledge of the operation of these systems become disempowered, unable to challenge or contest when it impacts their lives. In this context, accountability becomes a matter of building systems that can be challenged, interrogated, and, most importantly, adjusted in use to accommodate counter-intuitive results and unpredictable impacts. Thus, accountable system design can work to reconfigure socio-technical landscapes to protect the users of AI and to prevent unjust apportionment of risk. (shrink)

This chapter explores properties that bind individuals, knowledge, and communities, together. Section ‎‎1 introduces Hardwig’s argument from trust in others’ testimonies as entailing that trust is the glue ‎that binds individuals into communities. Section 2 asks “what grounds trust?” by exploring assessment ‎of collaborators’ explanatory responsiveness, formal indicators such as affiliation and credibility, ‎appreciation of peers’ tacit knowledge, game-theoretical considerations, and the role moral character ‎of peers, social biases, and social values play in grounding trust. Section 3 deals with establishing (...) ‎reliability standards for formation and breaching of trust. Different epistemic considerations and their ‎underpinning of inductive risks are examined through various communication routes within a ‎discipline, between disciplines, and to the public. Section 4 examines whether a collective entity can ‎be trusted over and above trust that is given to its individual members. Section 5 deals with the roles ‎technological artifacts play in distributed research and collective knowledge. It presents the common ‎view in which genuine trust cannot, in principle, be accorded to artifacts, so as an opposite view. We ‎show that what counts as a genuine object of trust is relevant to debates about the boundaries of ‎collective agency and as a criterion for extended cognitive systems.‎. (shrink)

Some of the systems used in natural language generation (NLG), a branch of applied computational linguistics, have the capacity to create or assemble somewhat original messages adapted to new contexts. In this paper, taking Bernard Williams’ account of assertion by machines as a starting point, I argue that NLG systems meet the criteria for being speech actants to a substantial degree. They are capable of authoring original messages, and can even simulate illocutionary force and speaker meaning. Background intelligence embedded in (...) their datasets enhances these speech capacities. Although there is an open question about who is ultimately responsible for their speech, if anybody, we can settle this question by using the notion of proxy speech, in which responsibility for artificial speech acts is assigned legally or conventionally to an entity separate from the speech actant. (shrink)

The synthetization of voices, or speech synthesis, has been an object of interest for centuries. It is mostly realized with a text-to-speech system, an automaton that interprets and reads aloud. This system refers to text available for instance on a website or in a book, or entered via popup menu on the website. Today, just a few minutes of samples are enough to be able to imitate a speaker convincingly in all kinds of statements. This article abstracts from actual products (...) and actual technological realization. Rather, after a short historical outline of the synthetization of voices, exemplary applications of this kind of technology are gathered for promoting the development, and potential applications are discussed critically to be able to limit them if necessary. The ethical and legal challenges should not be underestimated, in particular with regard to informational and personal autonomy and the trustworthiness of media. (shrink)