Alexa’s agency: a corpus-based study on the linguistic attribution of humanlikeness to voice user interfaces

AI and Society:1-15 (forthcoming)
  Copy   BIBTEX

Abstract

Voice-based, spoken interaction with artificial agents has become a part of everyday life in many countries: artificial voices guide us through our bank’s customer service, Amazon’s Alexa tells us which groceries we need to buy, and we can discuss central motifs in Shakespeare’s work with ChatGPT. Language, which is largely still seen as a uniquely human capacity, is now increasingly produced—or so it appears—by non-human entities, contributing to their perception as being ‘human-like.’ The capacity for language is far from the only prototypically human feature attributed to ‘speaking’ machines; their potential agency, consciousness, and even sentience have been widely discussed in the media. This paper argues that a linguistic analysis of agency (based on semantic roles) and animacy can provide meaningful insights into the sociocultural conceptualisations of artificial entities as humanlike actors. A corpus-based analysis investigates the varying attributions of agency to the voice user interfaces Alexa, Siri, and Google Assistant in German media data. The analysis provides evidence for the important role that linguistic anthropomorphisation plays in the sociocultural attribution of agency and consciousness to artificial technological entities, and how particularly the practise of using personal names for these devices contributes to the attribution of humanlikeness: it will be highlighted how Amazon’s Alexa and Apple’s Siri are linguistically portrayed as sentient entities who listen, act, and have a mind of their own, whilst the lack of a personal name renders the Google Assistant much more recalcitrant to anthropomorphism.

Other Versions

No versions found

Links

PhilArchive

    This entry is not archived by us. If you are the author and have permission from the publisher, we recommend that you archive it. Many publishers automatically grant permission to authors to archive pre-prints. By uploading a copy of your work, you will enable us to better index it, making it easier to find.

    Upload a copy of this work     Papers currently archived: 103,885

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Analytics

Added to PP
2025-03-15

Downloads
2 (#1,913,960)

6 months
2 (#1,353,553)

Historical graph of downloads
How can I increase my downloads?

Citations of this work

No citations found.

Add more citations