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A Machine's ethos? An inquiry into artificial ethos and trust.

Authors :
Skaug Sætra, Henrik
Source :
Computers in Human Behavior. Apr2024, Vol. 153, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

Every day we trust other individuals as we engage in social interactions in which various desirable outcomes depend on others acting the way we hope, or they have indicated. Trust extends beyond specific individuals, however, as we might trust unknown others – individuals, institutions, corporations, and governments. Some also say that we trust various artifacts, such as machines. But what is the basis of trust, and can we really trust technology? Trust is intimately connected to the notion ethos from the study of rhetoric and human persuasion, which is often used to describe various characteristics of the speaker, the audience, the relationship between the speaker and the audience, and the wider context in which communication and interaction occurs. In this article I explore to what degree machines can be considered to have ethos, and consequently whether ethos is a useful concept for understanding persuasive and credibility-related situations in HMI and by extension key aspects of human-machine trust. This allows us to draw on a long lineage of research from, for example, rhetoric, communication studies, and cognitive and social psychology to better understand the usefulness – or not – of using the notion of trust to describe our relationship with machines. • Trust in machines partly parallels trust in humans, institutions, and artifacts. • Ethos, from rhetoric, relates to credibility and trust in human-machine interaction. • This paper explores if machines can have ethos, and how this relates to trust. • Machine ethos is about perceived credibility and not the machine's ethics. • Examining ethos enriches understanding of trust and design in HMI. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
07475632
Volume :
153
Database :
Academic Search Index
Journal :
Computers in Human Behavior
Publication Type :
Academic Journal
Accession number :
174842020
Full Text :
https://doi.org/10.1016/j.chb.2023.108108