Colin Allen

As the above quote clearly highlights, it is the responsibility of researchers and research supervisors to be certain that their research staff and students assistants are very familiar with all of the ethical principles and current standards relevant to the research they are conducting. Indeed, they must take an active role in being certain that their research staff and students complete appropriate training in these ethical principles and standards, and how they apply them to the research context in which they are working. This is especially important in areas in which there may be physical harm such as chronic pain research. During the past decade, there has been a great increase in research of chronic pain, with breakthroughs in better understanding its etiology, assessment, and treatment (1,2). Obviously, much of this research was conducted using humans and animals as subjects. As a consequence, there were a number of ethical issues that investigators have to be cognizant of when conducting their studies. In this chapter, we will discuss such ethical issues in three major areas: (i) laboratory research with human subjects; (ii) laboratory research with animals; and (iii) translating these laboratory research ndings to ‘‘real world’’ applications in the clinical treatment arena

The implementation of moral decision making abilities in artificial intelligence (AI) is a natural and necessary extension to the social mechanisms of autonomous software agents and robots. Engineers exploring design strategies for systems sensitive to moral considerations in their choices and actions will need to determine what role ethical theory should play in defining control architectures for such systems. The architectures for morally intelligent agents fall within two broad approaches: the top-down imposition of ethical theories, and the bottom-up building of systems that aim at goals or standards which may or may not be specified in explicitly theoretical terms. In this paper we wish to provide some direction for continued research by outlining the value and limitations inherent in each of these approaches

Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational approaches to higher-order cognition. The need for increasingly autonomous artificial agents to factor moral considerations into their choices and actions has given rise to another new field of inquiry variously known as Machine Morality, Machine Ethics, Roboethics, or Friendly AI. In this study, we discuss how LIDA, an AGI model of human cognition, can be adapted to model both affective and rational features of moral decision making. Using the LIDA model, we will demonstrate how moral decisions can be made in many domains using the same mechanisms that enable general decision making. Comprehensive models of human cognition typically aim for compatibility with recent research in the cognitive and neural sciences. Global workspace theory, proposed by the neuropsychologist Bernard Baars (1988), is a highly regarded model of human cognition that is currently being computationally instantiated in several software implementations. LIDA (Franklin, Baars, Ramamurthy, & Ventura, 2005) is one such computational implementation. LIDA is both a set of computational tools and an underlying model of human cognition, which provides mechanisms that are capable of explaining how an agent’s selection of its next action arises from bottom-up collection of sensory data and top-down processes for making sense of its current situation. We will describe how the LIDA model helps integrate emotions into the human decision-making process, and we will elucidate a process whereby an agent can work through an ethical problem to reach a solution that takes account of ethically relevant factors.

The close kinship between humans, chimpanzees, gorillas, and orangutans is a central theme among participants in the debate about human treatment of the other apes. Empathy is probably the single most important determinant of actual human moral behavior, including the treatment of nonhuman animals. Given the applied nature of questions about the treatment of captive apes, it is entirely appropriate that the close relationship between us should be highlighted. But the role that relatedness should play in ethical theory is less clear. To the extent that legal and regulatory challenges to keeping apes in captivity are likely to be based on principles of theory, it is important to understand what roles evolutionary theory can play in deriving such principles. The development of ethically correct policies for captivity of animals will depend on taking into account both species-specific and individual differences in the ways that individuals perceive and conceptualize the spaces in which they live, and the choices with which they are presented. A fully evolutionary approach to cognition, a cognitive ethology, that is not just limited to the great apes or to primates is the best hope we have for understanding such perceptions and conceptions.

Ethicists have commonly appealed to science to bolster their arguments for elevating the moral status of nonhuman animals. I describe a framework within which I take many ethicists to be making such appeals. I focus on an apparent gap in this framework between those properties of animals that are part of the scientific consensus, and those to which ethicists typically appeal in their arguments. I will describe two different ways of diminishing the appearance of the gap, and argue that both of them present challenges to ethicists seeking a firm scientific basis for their claims about the moral status of animals. I argue that more clarity about the role of appeals to science by applied ethicists leads to questions about the effectiveness of such appeals, and that these questions might best be pursued empirically

We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ultimately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to the problem of identifying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology. We show how a combination of classification systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain. Through a novel approach of “drill-down” topic modeling—simultaneously reducing both the size of the corpus and the unit of analysis—we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historians and philosophers of comparative psychology. The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of “close reading” needed to generate original interpretations that is the heart of scholarly work in the humanities. Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes. The multilevel approach advances understanding of the intellectual and societal contexts in which writings are interpreted.

People can be taught to manipulate symbols according to formal mathematical and logical rules. Cognitive scientists have traditionally viewed this capacity—the capacity for symbolic reasoning—as grounded in the ability to internally represent numbers, logical relationships, and mathematical rules in an abstract, amodal fashion. We present an alternative view, portraying symbolic reasoning as a special kind of embodied reasoning in which arithmetic and logical formulae, externally represented as notations, serve as targets for powerful perceptual and sensorimotor systems. Although symbolic reasoning often conforms to abstract mathematical principles, it is typically implemented by perceptual and sensorimotor engagement with concrete environmental structures.

A principal goal of the discipline of artificial morality is to design artificial agents to act as if they are moral agents. Intermediate goals of artificial morality are directed at building into AI systems sensitivity to the values, ethics, and legality of activities. The development of an effective foundation for the field of artificial morality involves exploring the technological and philosophical issues involved in making computers into explicit moral reasoners. The goal of this paper is to discuss strategies for implementing artificial morality and the differing criteria for success that are appropriate to different strategies.

Few areas of scientific investigation have spawned more alternative approaches than animal behavior: comparative psychology, ethology, behavioral ecology, sociobiology, behavioral endocrinology, behavioral neuroscience, neuroethology, behavioral genetics, cognitive ethology, developmental psychobiology---the list goes on. Add in the behavioral sciences focused on the human animal, and you can continue the list with ethnography, biological anthropology, political science, sociology, psychology (cognitive, social, developmental, evolutionary, etc.), and even that dismal science, economics. Clearly, no reasonable-length chapter can do justice to such a varied collection. We have opted therefore to focus on three of these subdisciplines and to provide a somewhat historical tour of them, mentioning along the way the philosophical points that are of particular interest to us, but allowing the development of these points to be limited only by the imaginations of our readers. For readers seeking a more-traditional historical survey, see Dewsbury (1984a, b) and Burghardt (1985a). Our chosen brief is to write about comparative psychology, ethology, and cognitive ethology, although other approaches, especially neuroscience, will be mentioned where appropriate. These sciences are philosophically significant because they are enmeshed in ancient philosophical questions about the nature of mind and purposeful action and about the differences between humans and other animals. These sciences are also clustered because of their attention to mechanistic explanations of individual animal behavior as opposed to attempting to capture regularities at a population level, such as the game-theoretic strategic models popular among behavioral ecologists

The development of autonomous, robotic weaponry is progressing rapidly. Many observers agree that banning the initiation of lethal activity by autonomous weapons is a worthy goal. Some disagree with this goal, on the grounds that robots may equal and exceed the ethical conduct of human soldiers on the battlefield. Those who seek arms-control agreements limiting the use of military robots face practical difficulties. One such difficulty concerns defining the notion of an autonomous action by a robot. Another challenge concerns how to verify and monitor the capabilities of rapidly changing technologies. In this article we describe concepts from our previous work about autonomy and ethics for robots and apply them to military robots and robot arms control. We conclude with a proposal for a first step toward limiting the deployment of autonomous weapons capable of initiating lethal force

The application of digital humanities techniques to philosophy is changing the way scholars approach the discipline. This paper seeks to open a discussion about the difficulties, methods, opportunities, and dangers of creating and utilizing a formal representation of the discipline of philosophy. We review our current project, the Indiana Philosophy Ontology (InPhO) project, which uses a combination of automated methods and expert feedback to create a dynamic computational ontology for the discipline of philosophy. We argue that our distributed, expert-based approach to modeling the discipline carries substantial practical and philosophical benefits over alternatives. We also discuss challenges facing our project (and any other similar project) as well as the future directions for digital philosophy afforded by formal modeling

In this paper I argue that selection of the best theory of content is not a matter for mere philosophical reflection on the consequences of each theory for our intuitive judgments about content. Rather, the theories must be judged in a different way that is based on the putative roles of content attribution in the behavioural sciences. The ultimate test of any theory of content will be the success of the sciences that adopt it. Furthermore, alternative semantic theories may be seen as complementary rather than antagonistic. Different approaches to content specification may well be compatible, reflecting different but perhaps equally legitimate explanatory goals.

Psychology, according to a standard dictionary definition, is the science of mind and behavior. For a major part of the twentieth century, (nonhuman) animal psychology was on a behavioristic track that explicitly denied the possibility of a science of animal mind. While many comparative psychologists remain wedded to behavioristic methods, they have more recently adopted a cognitive, information-processing approach that does not adhere to the strictures of stimulus-response explanations of animal behavior. Cognitive ethologists are typically willing to go much further than comparative psychologists by adopting folk-psychological terms to explain the behavior of nonhuman animals. This different attitudes of many scientists presupposes a distinction between cognitive and mental state attributions that is not commonly articulated. This paper seeks to understand that distinction

In this paper, we approach the idea of group cognition from the perspective of the “extended mind” thesis, as a special case of the more general claim that systems larger than the individual human, but containing that human, are capable of cognition (Clark, 2008; Clark & Chalmers, 1998). Instead of deliberating about “the mark of the cognitive” (Adams & Aizawa, 2008), our discussion of group cognition is tied to particular cognitive capacities. We review recent studies of group problem-solving and group memory which reveal that specific cognitive capacities that are commonly ascribed to individuals are also aptly ascribed at the level of groups. These case studies show how dense interactions among people within a group lead to both similarity-inducing and differentiating dynamics that affect the group's ability to solve problems. This supports our claim that groups have organization-dependent cognitive capacities that go beyond the simple aggregation of the cognitive capacities of individuals. Group cognition is thus an emergent phenomenon in the sense of Wimsatt (1986). We further argue that anybody who rejects our strategy for showing that cognitive properties can be instantiated at multiple levels in the organizational hierarchy on a priori grounds is a “demergentist,” and thus incurs the burden of proof for explaining why cognitive properties are “stuck” at a certain level of organizational structure. Finally, we show that our analysis of group cognition escapes the “coupling-constitution” charge that has been leveled against the extended mind thesis (Adams & Aizawa, 2008).

  In this paper we argue that there is much to learn about “wild justice” and the evolutionary origins of morality – behaving fairly – by studying social play behavior in group-living mammals. Because of its relatively wide distribution among the mammals, ethological investigation of play, informed by interdisciplinary cooperation, can provide a comparative perspective on the evolution of ethical behavior that is broader than is provided by the usual focus on primate sociality. Careful analysis of social play reveals rules of engagement that guide animals in their social encounters. Because of its significance in development, play may provide a foundation of fairness for other forms of cooperation that are advantageous to group living. Questions about the evolutionary roots of cooperation, fairness, trust, forgiveness, and morality are best answered by attention to the details of what animals do when they engage in social play – how they negotiate agreements to cooperate, to forgive, to behave fairly, and to develop trust. We consider questions such as why play fairly? Why did play evolve as it has? Does “being fair” mean being more fit? Do individual variations in play influence an individual’s reproductive fitness? Can we use information about the foundations of moral behavior in animals to help us understand ourselves? We conclude that there is likely to be strong selection for cooperative fair play because there are mutual benefits when individuals adopt this strategy and group stability may also be fostered. Numerous mechanisms have evolved to facilitate the initiation and maintenance of social play, to keep others engaged, so that agreeing to play fairly and the resulting benefits of doing so can be readily achieved