Dialogue systems, from Virtual Personal Assistants such as Siri, Cortana, and Alexa to state-of-the-art systems such as BlenderBot3 and ChatGPT, are already widely available, used in a variety of applications, and are increasingly part of many people’s lives. However, the task of enabling them to use empathetic language more convincingly is still an emerging research topic. Such systems generally make use of complex neural networks to learn the patterns of typical human language use, and the interactions in which the systems (...) participate are usually mediated either via interactive text-based or speech-based interfaces. In human–human interaction, empathy has been shown to promote prosocial behaviour and improve interaction. In the context of dialogue systems, to advance the understanding of how perceptions of empathy affect interactions, it is necessary to bring greater clarity to how empathy is measured and assessed. Assessing the way dialogue systems create perceptions of empathy brings together a range of technological, psychological, and ethical considerations that merit greater scrutiny than they have received so far. However, there is currently no widely accepted evaluation method for determining the degree of empathy that any given system possesses (or, at least, appears to possess). Currently, different research teams use a variety of automated metrics, alongside different forms of subjective human assessment such as questionnaires, self-assessment measures and narrative engagement scales. This diversity of evaluation practice means that, given two DSs, it is usually impossible to determine which of them conveys the greater degree of empathy in its dialogic exchanges with human users. Acknowledging this problem, the present article provides an overview of how empathy is measured in human–human interactions and considers some of the ways it is currently measured in human–DS interactions. Finally, it introduces a novel third-person analytical framework, called the Empathy Scale for Human–Computer Communication (ESHCC), to support greater uniformity in how perceived empathy is measured during interactions with state-of-the-art DSs. (shrink)

Sequential transformative design of research :224–235, 2015; Groleau et al. in J Mental Health 16:731–741, 2007; Robson and McCartan in Real world research: a resource for users of social research methods in applied settings, Wiley, Chichester, 2016) allows testing a group of theoretical assumptions about the connections of artificial intelligence with culture and education. In the course of research, semiotics ensures the description of self-organizing systems of cultural signs and symbols in terms of artificial intelligence as a special set of (...) algorithms. This approach helps to consider the arguments proposed by Searle :417–457, 1980) against ‘strong’ artificial intelligence. Searle :417–457, 1980) believes that artificial or machine intelligence cannot fully emulate the processes of the human mind. Machine intelligence shows own inevitable weakness. This is non-autonomous tool for computations and data operating. In fact, this tool cannot provide insight into real cognitive conditions. After Lotman and Uspensky, authors expand the meaning of artificial intelligence. The authors identify a cultural type of ‘strong’ artificial intelligence or ‘self-increase of Logos’ in terms by Lotman and Uspensky. The interpretation of human intelligence as imitation of machine intelligence makes possible such immersion of artificial intelligence in culture. The authors reveal a case of self-organizing autonomous generation, encoding, decoding, reception, storage, and transmission of social information in the field of physical training. From the empirical studies it is clear that the organization of collective activities without external control ensures the development of positive emotions and social orientations. Interest in autonomous behavior provides the formation of educational and cognitive motives. As a special set of algorithms, these motives are the most promising and favorable for personal development. (shrink)

The phenomenon and ethics of “voting” will be explored in the context of human enhancements. “Voting” will be examined for enhanced humans with moderate and extreme enhancements. Existing patterns of discrimination in voting around the globe could continue substantially “as is” for those with moderate enhancements. For extreme enhancements, voting rights could be challenged if the very humanity of the enhanced was in doubt. Humans who were not enhanced could also be disenfranchised if certain enhancements become prevalent. Voting will be (...) examined using a theory of engagement articulated by Professor Sophie Loidolt that emphasizes the importance of legitimization and justification by “facing the appeal of the other” to determine what is “right” from a phenomenological first-person perspective. Seeking inspiration from the Universal Declaration of Human Rights (UDHR) of 1948, voting rights and responsibilities will be re-framed from a foundational working hypothesis that all enhanced and non-enhanced humans should have a right to vote directly. Representative voting will be considered as an admittedly imperfect alternative or additional option. The framework in which voting occurs, as well as the processes, temporal cadence, and role of voting, requires the participation from as diverse a group of humans as possible. Voting rights delivered by fiat to enhanced or non-enhanced humans who were excluded from participation in the design and ratification of the governance structure is not legitimate. Applying and extending Loidolt’s framework, we must recognize the urgency that demands the impossible, with openness to that universality in progress (or universality to come) that keeps being constituted from the outside. (shrink)

This essay discusses sentient robot research through the lens of suffering. First three kinds of suffering are considered: physical, psychological, and existential. Physical pain is shown to b...