The paper provides a selective analysis of the main theories of trust and e-trust (that is, trust in digital environments) provided in the last twenty years, with the goal of preparing the ground for a new philosophical approach to solve the problems facing them. It is divided into two parts. The first part is functional toward the analysis of e-trust: it focuses on trust and its definition and foundation and describes the general background on which the analysis of e-trust rests. (...) The second part focuses on e-trust, its foundation and ethical implications. The paper ends by synthesising the analysis of the two parts. (shrink)

Can we decide to trust? Sometimes, yes. And when we do, we need not believe that our trust will be vindicated. This paper is motivated by the need to incorporate these facts into an account of trust. Trust involves reliance; and in addition it requires the taking of a reactive attitude to that reliance. I explain how the states involved here differ from belief. And I explore the limits of our ability to trust. I then turn to the idea of (...) trusting what others say. I suggest that we sometimes decide to trust people to be sincere and knowledgeable; and that having taken this attitude towards them, we come to believe what they say. I spell out some consequences that this has for an account of testimony, and for van Fraassen's decision theoretic principle of Reflection. (shrink)

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)

Since approval of the EU General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that the GDPR will legally mandate a ‘right to explanation’ of all decisions made by automated or artificially intelligent algorithmic systems. This right to explanation is viewed as an ideal mechanism to enhance the accountability and transparency of automated decision-making. However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In contrast to the (...) right to explanation of specific automated decisions claimed elsewhere, the GDPR only mandates that data subjects receive meaningful, but properly limited, information (Articles 13-15) about the logic involved, as well as the significance and the envisaged consequences of automated decision-making systems, what we term a ‘right to be informed’. Further, the ambiguity and limited scope of the ‘right not to be subject to automated decision-making’ contained in Article 22 (from which the alleged ‘right to explanation’ stems) raises questions over the protection actually afforded to data subjects. These problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless. We propose a number of legislative and policy steps that, if taken, may improve the transparency and accountability of automated decision-making when the GDPR comes into force in 2018. (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)

Engineers are traditionally regarded as trustworthy professionals who meet exacting standards. In this chapter I begin by explicating our trust relationship towards engineers, arguing that it is a linear but indirect relationship in which engineers “stand behind” the artifacts and technological systems that we rely on directly. The chapter goes on to explain how this relationship has become more complex as engineers have taken on two additional aims: the aim of social engineering to create and steer trust between people, and (...) the aim of creating automated systems that take over human tasks and are meant to invite the trust of those who rely on and interact with them. (shrink)

Trust is central to our social lives. We know by trusting what others tell us. We act on that basis, and on the basis of trust in their promises and implicit commitments. So trust underpins both epistemic and practical cooperation and is key to philosophical debates on the conditions of its possibility. It is difficult to overstate the significance of these issues. On the practical side, discussions of cooperation address what makes society possible—of how it is that life is not (...) a Hobbesian war of all against all. On the epistemic side, discussions of cooperation address what makes the pooling of knowledge possible—and so the edifice that is science. But trust is not merely central to our lives instrumentally; trusting relations are themselves of great value and, in trusting others, we realize distinctive forms of value. What are these forms of value, and how is trust central to our lives? These questions are explored and developed in this volume, which collects fifteen new essays on the philosophy of trust. They develop and extend existing philosophical discussion of trust and will provide a reference point for future work on trust. (shrink)

Moral Repair examines the ethics and moral psychology of responses to wrongdoing. Explaining the emotional bonds and normative expectations that keep human beings responsive to moral standards and responsible to each other, Margaret Urban Walker uses realistic examples of both personal betrayal and political violence to analyze how moral bonds are damaged by serious wrongs and what must be done to repair the damage. Focusing on victims of wrong, their right to validation, and their sense of justice, Walker presents a (...) unified and detailed philosophical account of hope, trust, resentment, forgiveness, and making amends - the emotions and practices that sustain moral relations. Moral Repair joins a multidisciplinary literature concerned with transitional and restorative justice, reparations, and restoring individual dignity and mutual trust in the wake of serious wrongs. (shrink)

Trust is a kind of risky reliance on another person. Social scientists have offered two basic accounts of trust: predictive expectation accounts and staking (betting) accounts. Predictive expectation accounts identify trust with a judgment that performance is likely. Staking accounts identify trust with a judgment that reliance on the person’s performance is worthwhile. I argue (1) that these two views of trust are different, (2) that the staking account is preferable to the predictive expectation account on grounds of intuitive adequacy (...) and coherence with plausible explanations of action; and (3) that there are counterexamples to both accounts. I then set forward an additional necessary condition on trust, according to which trust implies a moral expectation. The content of the moral expectation is this: W hen A trusts B to do x, A ascribes an obligation to B to do x, and holds B to this obligation. This moral expectation account throws new light on some of the consequences of misplaced trust. I use the example of physicians’ defensive behavior to illustrate this final point. (shrink)

There is a growing literature on the concept of e-trust and on the feasibility and advisability of “trusting” artificial agents. In this paper we present an object-oriented model for thinking about trust in both face-to-face and digitally mediated environments. We review important recent contributions to this literature regarding e-trust in conjunction with presenting our model. We identify three important types of trust interactions and examine trust from the perspective of a software developer. Too often, the primary focus of research in (...) this area has been on the artificial agents and the humans they may encounter after they are deployed. We contend that the humans who design, implement, and deploy the artificial agents are crucial to any discussion of e-trust and to understanding the distinctions among the concepts of trust, e-trust and face-to-face trust. (shrink)

This essay discusses the nature of collective responsibility and explores various controversies associated with its possibility and normative value.

A program for the simulation of rational social normative trust, predictive `trust,' and predictive reliance between agents will be introduced. It offers a tool for social scientists or a trust component for multi-agent simulations/multi-agent systems, which need to include trust between agents to guide the decisions about the course of action. It is based on an analysis of rational social normative trust (RSNTR) (revised version of M. Tuomela 2002), which is presented and briefly argued. For collective agents, belief conditions for (...) collective agency should be added. For the various forms of trust agents must have (at least) subjectively rational reasons to believe that the conditions of the trust account are fulfilled. A list of such reasons (of varied weights), e.g., given by empirical research, can manually be built into a parameter file or be generated by a calling program in a fixed format. From this list of reasons the program randomly generates a belief base for the agents of the artificial society. Reasons can be chained together so that one set of reasons satisfies several belief conditions. The program checks if the conditions are fulfilled for the artificial agents' social normative trust/predictive `trust'/`predictive reliance' in another agent that he will perform an action X. Each outcome is logged to a result file. In conclusion we discuss various aspects of the application of a trust component of the suggested kind in empirical research, social simulation, and multi-agent systems. (shrink)

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable (...) and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. (shrink)

We argue that the notion of trust, as it figures in an ethical context, can be illuminated by examining research in artificial intelligence on multi-agent systems in which commitment and trust are modeled. We begin with an analysis of a philosophical model of trust based on Richard Holton’s interpretation of P. F. Strawson’s writings on freedom and resentment, and we show why this account of trust is difficult to extend to artificial agents (AAs) as well as to other non-human entities. (...) We then examine Margaret Urban Walker’s notions of “default trust” and “default, diffuse trust” to see how these concepts can inform our analysis of trust in the context of AAs. In the final section, we show how ethicists can improve their understanding of important features in the trust relationship by examining data resulting from a classic experiment involving AAs. (shrink)