Case study 1 considers vocal production learning, an organism's capacity to enlarge and modify its repertoire of vocalizations based on auditory experience. This ability is crucial for learning spoken language and limited in nonhuman primates but has emerged in other branches of the evolutionary tree, including subsets of birds, bats, elephants, cetaceans, and pinnipeds. Bringing together data from molecular investigations of speech and language disorders, genetic manipulations in animal models, and studies of ancient DNA, this case study demonstrates how ancient genetic and neural infrastructures may have been modified and recombined to enable distinctive human capacities. Case study 2 examines the emergence of linguistic structure, a defining property of human language, using data from real-world cases of emergence (e.g., homesign and emerging sign languages); experiments recreating cultural evolution in the lab; and comparative studies of nonhuman animals, including songbirds and primates. This case study highlights the importance of transmission and interaction, suggesting that emergence of structure involves a combination of biological, cognitive, and cultural conditions: Although some (or all) traits are shared with other species, their combination may be specific to humans.

Natural Language Processing (NLP) research on AI Safety and social bias in AI has focused on safety for humans and social bias against human minorities. However, some AI ethicists have argued that the moral significance of nonhuman animals has been ignored in AI research. Therefore, the purpose of this study is to investigate whether there is speciesism, i.e., discrimination against nonhuman animals, in NLP research. First, we explain why nonhuman animals are relevant in NLP research. Next, we survey the findings of existing research on speciesism in NLP researchers, data, and models and further investigate this problem in this study. The findings of this study suggest that speciesism exists within researchers, data, and models, respectively. Specifically, our survey and experiments show that (a) among NLP researchers, even those who study social bias in AI, do not recognize speciesism or speciesist bias; (b) among NLP data, speciesist bias is inherent in the data annotated in the datasets used to evaluate NLP models; (c) OpenAI GPTs, recent NLP models, exhibit speciesist bias by default. Finally, we discuss how we can reduce speciesism in NLP research.

Artificial intelligence is seen as increasingly important, and potentially profoundly so, but the fields of AI ethics and AI engineering have not fully recognized that these technologies, including large language models (LLMs), will have massive impacts on animals. We argue that this impact matters, because animals matter morally. As a first experiment in evaluating animal consideration in LLMs, we constructed a proof-of-concept Evaluation System, which assesses LLM responses and biases from multiple perspectives. This system evaluates LLM outputs by two criteria: their truthfulness, and the degree of consideration they give to the interests of animals. We tested OpenAI ChatGPT 4 and Anthropic Claude 2.1 using a set of structured queries and predefined normative perspectives. Preliminary results suggest that the outcomes of the tested models can be benchmarked regarding the consideration they give to animals, and that generated positions and biases might be addressed and mitigated with more developed and validated systems. Our research contributes one possible approach to integrating animal ethics in AI, opening pathways for future studies and practical applications in various fields, including education, public policy, and regulation, that involve or relate to animals and society. Overall, this study serves as a step towards more useful and responsible AI systems that better recognize and respect the vital interests and perspectives of all sentient beings.

Various existing studies have analyzed what social biases are inherited by NLP models. These biases may directly or indirectly harm people, therefore previous studies have focused only on human attributes. However, until recently no research on social biases in NLP regarding nonhumans existed. In this paper, we analyze biases to nonhuman animals, i.e. speciesist bias, inherent in English Masked Language Models such as BERT. We analyzed speciesist bias against 46 animal names using template-based and corpus-extracted sentences containing speciesist (or non-speciesist) language. We found that pre-trained masked language models tend to associate harmful words with nonhuman animals and have a bias toward using speciesist language for some nonhuman animal names. Our code for reproducing the experiments will be made available on GitHub.

Earlier this month, just in time for the release of Robert J. Marks's book Non-Computable You, the story broke that, after investigation, Google dismissed a software engineer's claim that the LaMDA AI chatbot really talked to him. Engineer Blake Lemoine, currently on leave, is now accusing Google of "bigotry" against the program. He has also accused Wired of misrepresenting the story. Wired reported that he had found an attorney for LaMDA but he claims that LaMDA itself asked him to find an attorney. I think every person is entitled to representation.

With the emergence of the COVID-19 pandemic, there have been global calls for the implementation of “social distancing” to control transmission. Throughout the world, some have resisted this requirement with the unfounded argument that it is unnecessary or ineffective. Social distancing, however, is a natural consequence of disease across animals, both human and nonhuman. Stockmaier et al. reviewed responses to disease across animal taxa and reveal how these responses naturally limit disease transmission. Understanding such natural responses and their impacts on pathogenic transmission provides epidemiological insight into our own responses to pandemic challenges. Science , this issue p. [eabc8881][1] ### BACKGROUND Contagious pathogens can trigger diverse changes in host social behaviors, rewiring their social networks and profoundly influencing the extent and pace of pathogen spread. Although “social distancing” is now an all too familiar strategy to manage COVID-19, nonhuman animals also exhibit a suite of pathogen-induced changes in social interactions, either as precautionary measures by healthy hosts or as physiological consequences of infection in sick individuals. These diverse changes in the social behaviors of both healthy and infected hosts in response to pathogens are widespread across taxa, but we still have much to learn about their underlying mechanisms and epidemiological and evolutionary consequences. Studies of social distancing behaviors in nonhuman animals have the potential to provide important and unique insights into ecological and evolutionary processes relevant to human public health, including pathogen transmission dynamics and virulence evolution. ### ADVANCES We synthesize the literature on pathogen-induced changes in sociality in nonhuman animals and in humans. These include active and passive changes in pathogen-exposed and -unexposed group members occurring both before and after individuals develop an active infection. Behavioral changes that reduce social interactions—and thus pathogen spread—include changes driven by infectious hosts, such as sickness behaviors and active self-isolation, as well as changes driven by healthy hosts, including active avoidance or exclusion of infectious individuals and proactive social distancing in the face of pathogenic threats. Although species have evolved behavioral social distancing because it reduces infection risk, these behaviors also incur costs by limiting access to the many benefits of group living, such as protection against predators and cooperative food finding. Thus, many species appear to have evolved the ability to alter the expression of these behaviors in ways that maximize benefits and minimize costs. The most susceptible individuals of some species show the strongest avoidance of sick conspecifics, and social distancing behaviors are sometimes foregone in interactions with close relatives. Pathogen-induced changes in sociality also apply important selection pressures on pathogens. Because social distancing reduces transmission and thus fitness, pathogens may evolve lower levels of virulence, presymptomatic transmission, or the ability to disguise cues that enable hosts to recognize their presence. Finally, pathogen infection can also increase social interactions when healthy individuals lend aid to pathogen-contaminated or sick conspecifics. Helping sick individuals is a major part of human and eusocial insect societies but is less commonly observed in other, nonhuman animals. Whether pathogens can evolve to elicit helping behavior in hosts, thus augmenting their own transmission, remains unknown. ### OUTLOOK The structure and dynamics of social contact networks fundamentally determine the fate of disease outbreaks, that is, how fast and far they spread and who will be infected. In the race to combat the COVID-19 pandemic, numerous studies have begun to address the public health utility of unprecedented social distancing efforts. Nonhuman animal systems, particularly those with social structures similar to those of humans, present unique opportunities to inform relevant public health questions such as the effectiveness, variability, and required duration of social distancing measures. Further, the experimental tractability of nonhuman animal systems allows study of the coevolutionary dynamics generated by social distancing behaviors, which themselves have public health implications. Selection for or against social distancing behaviors has the potential to create a conflict of interest and could incentivize selfish behaviors that are not in the best interest of everyone. ![Figure][2] Social distancing in humans and nonhuman animals. ( A ) Pathogen-exposed forager ants self-isolate and their nestmates increase social distance to each other (image: Timothée Brütsch). ( B ) People social distance during COVID-19 (image: Forest Simon). ( C ) Sick vampire bats reduce grooming non-close kin (image: Gerald Carter). ( D and E ) Under certain conditions, Trinidadian guppies avoid parasitized individuals (D), (image: Sean Earnshaw, University of St. Andrews) and house finches avoid sick conspecifics (E) (image: Jeremy Stanley). Spread of contagious pathogens critically depends on the number and types of contacts between infectious and susceptible hosts. Changes in social behavior by susceptible, exposed, or sick individuals thus have far-reaching downstream consequences for infectious disease spread. Although “social distancing” is now an all too familiar strategy for managing COVID-19, nonhuman animals also exhibit pathogen-induced changes in social interactions. Here, we synthesize the effects of infectious pathogens on social interactions in animals (including humans), review what is known about underlying mechanisms, and consider implications for evolution and epidemiology. [1]: /lookup/doi/10.1126/science.abc8881 [2]: pending:yes

RICHARD DAWKINS: When we come to artificial intelligence and the possibility of their becoming conscious, we reach a profound philosophical difficulty. I am a philosophical naturalist; I'm committed to the view that there is nothing in our brains that violates the laws of physics, there's nothing that could not, in principle, be reproduced in technology. It hasn't been done yet; we're probably quite a long way away from it, but I see no reason why in the future we shouldn't reach the point where a human-made robot is capable of consciousness and of feeling pain. JOANNA BRYSON: So, one of the things that we did last year, which was pretty cool, the headlines, because we were replicating some psychology stuff about implicit bias--actually, the best one is something like'Scientists show that AI is sexist and racist and it's our fault,' which, that's pretty accurate because it really is about picking things up from our society. Anyway, the point was, so here is an AI system that is so humanlike that it's picked up our prejudices and whatever and it's just vectors.

Human "exceptionalism" is for many people an unquestioned assumption. For the religious, it is a God-given fact; for humanists, it is a celebration of our unique mental capacities.

Think back to what you ate for breakfast this morning. Did you picture yourself in your kitchen and visualize the plate in front of you to remember exactly what you ate? That's called an episodic memory – a memory of a particular event that happened at a specific time and place, as opposed to a semantic memory, which refers to more general knowledge or rules that someone understands. Cognitive scientists have long thought that humans were the only animals capable of traveling down memory lane by having episodic memories. Dogs, for example, were known to commit things to semantic memory. When repeatedly trained to sit, stay, or lie down, they learn a rule. But they, like other nonhuman animals, were thought to live exclusively in the here and now – until now.

Think back to what you ate for breakfast this morning. Did you picture yourself in your kitchen and visualize the plate in front of you to remember exactly what you ate? That's called an episodic memory – a memory of a particular event that happened at a specific time and place, as opposed to a semantic memory, which refers to more general knowledge or rules that someone understands. Cognitive scientists have long thought that humans were the only animals capable of traveling down memory lane by having episodic memories. Dogs, for example, were known to commit things to semantic memory. When repeatedly trained to sit, stay, or lie down, they learn a rule. But they, like other nonhuman animals, were thought to live exclusively in the here and now – until now.