Masashi Takeshita

This paper explores the ways in which large language models (LLMs) can be used for the purpose of experimental philosophy. LLMs can be used in different ways for different purposes in experimental philosophy (Section 2). Among others, we focus on what we call the “non-predictive” research, in which LLMs’ “intuitive” responses to thought experiment cases are not expected to be predictive of, or analogous to, human subjects’ intuitive responses to them (Section 3). In effect, the non-predictive research regards LLMs’ responses as superior to human subjects’ responses, at least for the purpose of experimental philosophy. We argue that the non-predictive use of LLMs in experimental philosophy is highly motivated. Firstly, human subjects’ intuitive responses tend to be vulnerable to irrelevant factors and conceptual misunderstandings, which raises a serious worry concerning the reliability of their responses. Secondly, it is possible in the near future that LLMs can overcome these problematic factors to which human subjects are subject. As a case study, we conducted an experimental study in which we examined LLM’s “intuitions” on free will and determinism (Section 4). Our mixed results suggest that while current LLMs outperform human participants in terms of comprehension—especially in understanding deterministic scenarios—they remain highly sensitive to irrelevant factors such as framing and question order. These findings indicate that although LLMs have promising potential, they are not yet reliable sources of stable and unbiased philosophical intuitions. We conclude that substantial technical improvements are required before artificial intuitions can serve as superior alternatives to human intuitions in philosophical research.

In the ethical literature on robot design, considerable attention has been devoted to the risks that human-like robots pose to humans. In particular, the resemblance of robots to humans has been associated with concerns about social disruption, deception, and potential harm, leading some authors to argue for restrictions or bans in certain contexts. However, while the ethical significance of human-like form has been widely recognized for humans, significantly less attention has been paid to the implications of animal-like robots from the perspective of nonhuman animals and human–animal relations. This paper addresses that gap by examining how the design and deployment of animal-like robots may affect animals both directly and indirectly. We argue that the use of animal form in robotics is not ethically neutral, as it can shape human perceptions of animals, influence patterns of interaction, and potentially contribute to harms such as mislearning, objectification, and alienation. By shifting the focus from human-centered concerns to the impact on animals, we identify a set of ethically relevant risks associated with animal-like robots and analyze their broader implications for human–animal relations. On this basis, we propose preliminary guidelines aimed at supporting the responsible design and use of such technologies.

Philosophers commonly use thought experiments to elicit intuitions that serve as evidence for or against philosophical theories. Daniel Dennett calls such thought experiments “intuition pumps” and expresses concern that some of them are “deceptive” in that they elicit conflicting intuitions and thus lead to conflicting conclusions. What, then, should philosophers do with “deceptive” intuition pumps? Dennett proposes what might be called restrictionism, according to which philosophers should restrict the use of deceptive intuition pumps. In this paper, however, we argue that it faces what we call the problem of over-restriction: if philosophers restrict the use of deceptive intuition pumps, then they end up restricting the use of so many intuition pumps that they cannot continue philosophical inquiry. Then, as an alternative, we propose reformism, according to which philosophers should continue the use of deceptive intuition pumps after reforming them. We argue that reformism is more attractive than restrictionism, because it deals with the deceptiveness of intuition pumps while avoiding the problem of over-restriction. Overall, through criticizing Dennett’s restrictionism, we aim to illuminate the attractiveness of reformism, which has been underappreciated as a response to the deceptiveness of intuition pumps, and to call for future research in the reformist research program.

The world faces various problems, such as starvation and infectious diseases. One approach to solving these problems is Effective Altruism (EA), which seeks to make the world as good as possible based on objective evidence and careful reasoning. EA has been supported by utilitarians such as Peter Singer. In fact, many people who support EA are utilitarian. However, in recent years, some previous studies have defended EA in non-utilitarian approaches. This paper argues that EA is justified not only from utilitarianism but also from other forms of consequentialism and a Kantian perspective, which existing studies have not yet examined. This paper also discusses the difference between consequentialism and the Kantian perspective in supporting EA. First, this paper introduces and briefly explains William MacAskill’s definition of EA. According to that definition, an EA act maximizes the good based on careful reasoning and objective evidence. Next, this paper shows that the practice of EA is justified by some form of consequentialism, which takes the consequence of an action as the basis for moral evaluation. Second, a Kantian perspective requires altruistic acts to promote the happiness of others as a duty. This paper focuses on two points to suggest that altruistic acts from Kantian duty can meet the definition of EA acts. 1. non-intuitive and non-emotional reasoning about the happiness of others; and 2. taking an impartial standpoint. From these points, this paper shows that from a Kantian perspective, it is argued that we must aim to help others and act in a way that promotes the well-being pursued by others as far as possible. The last part of this paper explores the differences in approach when justifying EA acts from a consequentialist or Kantian perspective. Specifically, this section compares the selection of donation recipients and the level of self-sacrifice under each viewpoint.

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.