De-anthropomorphizing “AI”: From wishful mnemonics to accurate nomenclature
- ,
- Peter Zukerman,
- Emily M. Bender
- ,
- DCS Corporation,
- University of Washington
Research Output:
Journal Article or Conference Article in Journal
Journal article
Peer-reviewOpen access
Publication Information
Output type
Research Output:
Journal Article or Conference Article in Journal
Journal article
Peer-reviewOriginal language
EnglishJournal (Volume, Issue Number)
First Monday (Volume 31, Issue 2)Publication milestones
- Published - 02/02/2026
Publication status
Published - 02/02/2026
ISSN
1396-0466Publication IDs
- ORCID: /0000-0002-5375-9542/work/204590633
- Scopus: 105030871505
Abstract
Language matters. How we describe “AI” technology influences how it is perceived, deployed, and trusted. Extravagant and persuasive language incites hype. It is the responsibility of journalists, companies, and scholars to characterize technology in ways that inform and empower their readers by using appropriate terminology and avoiding inflated claims. One type of inflated claim comes from using anthropomorphizing language to describe system functionality. Anthropomorphization is the attribution of human capabilities and characteristics to an inanimate system. In this paper, we present a linguistic analysis of anthropomorphizing language in 29 texts (a total of 1,368 sentences) from academic articles, online news articles, and company blog posts. We construct a taxonomy of eight categories of anthropomorphization: Cognizer, Products of cognition, Emotion, Communication, Agent, Human role analogy, Names and pronouns, and Biological metaphors. Following this taxonomy we present concrete strategies for how to de-anthropomorphize the language we use to describe “AI” based on a functionality-first principle.
Publication metrics
PlumX, opens in new tab
Captures
9
Citations
3
Social media
7
Mentions
1
