Human attitudes to animals have changed as non-humans have become more widely incorporated in the category of moral agents who deserve some respect. Parallels between the functioning of humans and non-humans have been made for thousands of years but the idea that the animals that we keep can suffer has spread recently. An improved understanding of motivation, cognition and the complexity of social behaviour in animals has led in the last 30 years to the rapid development of animal welfare science. (...) Early attempts to define welfare referred to individuals being in harmony with nature but the first usable definition incorporated feelings and health as part of attempts to cope with the environment. Others considered that welfare is only about feelings but it is argued that as feelings are mechanisms that have evolved they are a part of welfare rather than all of it. Most reviews of welfare now start with listing the needs of the animal, including needs to show certain behaviours. This approach has used sophisticated studies of what is important to animals and has replaced the earlier general guidelines described as freedoms. Many measures of welfare are now used and indicate how good or how poor the welfare is. Naturalness is not a part of the definition of welfare but explains why some needs exist. In recent years, welfare has become established as one of various criteria used to decide on whether a system is sustainable because members of the public will not accept systems that cause poor welfare. The study of welfare has become part of the scientific basis upon which important political decisions are made. (shrink)

A major approach to the ethics of artificial intelligence is to use social choice, in which the AI is designed to act according to the aggregate views of society. This is found in the AI ethics of “coherent extrapolated volition” and “bottom–up ethics”. This paper shows that the normative basis of AI social choice ethics is weak due to the fact that there is no one single aggregate ethical view of society. Instead, the design of social choice AI faces three (...) sets of decisions: standing, concerning whose ethics views are included; measurement, concerning how their views are identified; and aggregation, concerning how individual views are combined to a single view that will guide AI behavior. These decisions must be made up front in the initial AI design—designers cannot “let the AI figure it out”. Each set of decisions poses difficult ethical dilemmas with major consequences for AI behavior, with some decision options yielding pathological or even catastrophic results. Furthermore, non-social choice ethics face similar issues, such as whether to count future generations or the AI itself. These issues can be more important than the question of whether or not to use social choice ethics. Attention should focus on these issues, not on social choice. (shrink)

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. (shrink)

In spite of the considerable literature nowadays existing on the issue of the moral exclusion of nonhuman animals, there is still work to be done concerning the characterization of the conceptual framework with which this question can be appraised. This paper intends to tackle this task. It starts by defining speciesism as the unjustified disadvantageous consideration or treatment of those who are not classified as belonging to a certain species. It then clarifies some common misunderstandings concerning what this means. Next, (...) it rejects the idea that there are different kinds of speciesism. Such an idea may result from confusion because there are (1) different ways in which speciesism can be defended; and (2) different speciesist positions, that is, different positions that assume speciesism among their premises. Depending on whether or not these views assume other criteria for moral consideration apart from speciesism, they can be combined or simple speciesist positions. But speciesism remains in all cases the same idea. Finally, the paper examines the concept of anthropocentrism, the disadvantageous treatment or consideration of those who are not members of the human species. This notion must be conceptually distinguished from speciesism and from misothery (aversion to nonhuman animals). Anthropocentrism is shown to be refuted because it either commits a petitio principia fallacy or it falls prey to two arguments: the argument from species overlap (widely but misleadingly known as “argument from marginal cases”) and the argument from relevance. This rebuttal identifies anthropocentrism as a speciesist view. (shrink)

One challenge to the idea that animals have a moral right to life claims that any such right would require us to intervene in the wild to prevent animals from being killed by predators. I argue that belief in an animal right to life does not commit us to supporting a program of predator-prey intervention. One common retort to the predator challenge contends that we are not required to save animals from predators because predators are not moral agents. I suggest (...) that this retort fails to overcome the predator challenge. I seek to articulate a more satisfactory argument explaining why we are not required to save wild prey from predators and how this position is perfectly consistent with the idea that animals have a basic right to life. (shrink)

The history of the regulation of animal research is essentially the history of the emergence of meaningful social ethics for animals in society. Initially, animal ethics concerned itself solely with cruelty, but this was seen as inadequate to late 20th-century concerns about animal use. The new social ethic for animals was quite different, and its conceptual bases are explored in this paper. The Animal Welfare Act of 1966 represented a very minimal and in many ways incoherent attempt to regulate animal (...) research, and is far from morally adequate. The 1985 amendments did much to render coherent the ethic for laboratory animals, but these standards were still inadequate in many ways, as enumerated here. The philosophy underlying these laws is explained, their main provisions are explored, and future directions that could move the ethic forward and further rationalize the laws are sketched. (shrink)

Pigeons and other animals sometimes deviate from optimal choice behavior when given informative signals for delayed outcomes. For example, when pigeons are given a choice between an alternative that always leads to food after a delay and an alternative that leads to food only half of the time after a delay, preference changes dramatically depending on whether the stimuli during the delays are correlated with the outcomes or not. With signaled outcomes, pigeons show a much greater preference for the suboptimal (...) alternative than with unsignaled outcomes. Key variables and research findings related to this phenomenon are reviewed, including the effects of durations of the choice and delay periods, probability of reinforcement, and gaps in the signal. We interpret the available evidence as reflecting a preference induced by signals for good news in a context of uncertainty. Other explanations are briefly summarized and compared. (shrink)

This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, which combines these elements (...) in a systematic way, has considerable advantages in this context. Third, the central challenge for theorists is not to identify ‘true’ moral principles for AI; rather, it is to identify fair principles for alignment that receive reflective endorsement despite widespread variation in people’s moral beliefs. The final part of the paper explores three ways in which fair principles for AI alignment could potentially be identified. (shrink)

Because of accelerating technological progress, humankind may be rapidly approaching a critical phase in its career. In addition to well-known threats such as nuclear holocaust, the propects of radically transforming technologies like nanotech systems and machine intelligence present us with unprecedented opportunities and risks. Our future, and whether we will have a future at all, may well be determined by how we deal with these challenges. In the case of radically transforming technologies, a better understanding of the transition dynamics from (...) a human to a "posthuman" society is needed. Of particular importance is to know where the pitfalls are: the ways in which things could go terminally wrong. While we have had long exposure to various personal, local, and endurable global hazards, this paper analyzes a recently emerging category: that of existential risks. These are threats that could case our extinction or destroy the potential of Earth - originating intelligent life. Some of these threats are relatively well known while others, including some of the gravest, have gone almost unrecognized. Existential risks have a cluster of features that make ordinary risk management ineffective. A final section of this paper discusses several ethical and policy implications. A clearer understanding of the threat picture will enable us to formulate better strategies. (shrink)