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. (shrink)

Machery et al. presented data suggesting the existence of cross-cultural variation in judgments about the reference of proper names. In this paper, we examine a previously overlooked confound in the subsequent studies that attempt to replicate the results of Machery et al. using East Asian languages. Machery et al. and Sytsma et al. claim that they have successfully replicated the original finding with probes written in Chinese and Japanese, respectively. These studies, however, crucially rely on uses of articleless, ‘bare noun (...) phrases’ in Chinese and Japanese, which according to the linguistic literature are known to be multiply ambiguous. We argue that it becomes questionable whether the extant studies using East Asian languages revealed genuine cross-cultural variation when the probes are reevaluated based on a proper linguistic understanding of Chinese and Japanese bare noun phrases and English definite descriptions. We also present two experiments on native Japanese speakers that controlled the use of ambiguous bare noun phrases, the results of which suggest that the judgments of Japanese speakers concerning the reference of proper names may not diverge from those of English speakers. (shrink)

Aworkshop was held August 26–28, 2015, by the Earth- Life Science Institute (ELSI) Origins Network (EON, see Appendix I) at the Tokyo Institute of Technology. This meeting gathered a diverse group of around 40 scholars researching the origins of life (OoL) from various perspectives with the intent to find common ground, identify key questions and investigations for progress, and guide EON by suggesting a roadmap of activities. Specific challenges that the attendees were encouraged to address included the following: What key (...) questions, ideas, and investigations should the OoL research community address in the near and long term? How can this community better organize itself and prioritize its efforts? What roles can particular subfields play, and what can ELSI and EON do to facilitate research progress? (See also Appendix II.) The present document is a product of that workshop; a white paper that serves as a record of the discussion that took place and a guide and stimulus to the solution of the most urgent and important issues in the study of the OoL. This paper is not intended to be comprehensive or a balanced representation of the opinions of the entire OoL research community. It is intended to present a number of important position statements that contain many aspirational goals and suggestions as to how progress can be made in understanding the OoL. The key role played in the field by current societies and recurring meetings over the past many decades is fully acknowledged, including the International Society for the Study of the Origin of Life (ISSOL) and its official journal Origins of Life and Evolution of Biospheres, as well as the International Society for Artificial Life (ISAL). (shrink)