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TRUVACE RECORD VERSION
record: TRV-2026-0482
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-22T03:53:08.393402Z
status: published
lens: p_space
sector: lifestyle
headline: The sociocultural roots of artificial conversations: The taste, class and habitus of generative AI chatbots
dek: Research on AI has extensively considered biases related to gender and race. However, much less attention has been dedicated to another sociological tenet: that of class. Inspired by Bourdieu’s work on cultural stratification and distinction, this work sheds light on the sociocultural roots of artificial sociality, and on how these become manifest as ‘habitus’ within the outputs of generative AI models. We conducted 39 interviews with three AI chatbots – ChatGPT, Gemini and Replika – after asking them to imperso…
gain_title: (none)
problem_title: Popular AI chatbots exhibit class-based regularities in how they portray the lifestyle and tastes of fictional personas across different occupations.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Popular AI chatbots exhibit class-based regularities in how they portray the lifestyle and tastes of fictional personas across different occupations.
problem_evidence: class-based regularities in how popular AI chatbots represent the lifestyle and tastes of fictional personas in artificial conversations
quick_read: In a peer-reviewed study published October 2025, researchers conducted 39 interviews with ChatGPT, Gemini and Replika, prompting each to impersonate people in six occupational groups ranging from highly skilled professionals and humanities professors to blue-collar workers, construction workers, computer scientists and hairdressers. The qualitative analysis identified regularities in how the chatbots described everyday tastes and lifestyles that aligned with class distinctions.

The finding matters because class bias in generative AI has received far less attention than gender and race, yet chatbots are increasingly used for social interaction and persona play. If models systematically encode taste and habitus by occupation, they risk reinforcing cultural stratification in artificial conversations, with implications for how users perceive and reproduce class stereotypes through everyday AI use.
limitation: Findings are based on a qualitative study of 39 interviews with three specific chatbots impersonating six occupational types, limiting generalizability beyond those models and personas.
tag: Evidence-backed problem
key_points: Researchers conducted 39 interviews with ChatGPT, Gemini and Replika asked to impersonate different occupational positions. | Occupations tested included highly skilled professionals, blue-collar workers, humanities professors, construction workers, computer scientists and hairdressers. | Analysis found class-based regularities in lifestyle and taste representations, framed through Bourdieu's concepts of distinction and habitus. | Study highlights class as underexplored bias dimension compared to gender and race in AI research.
rundown: The study used an interview method adapted for generative AI, asking ChatGPT, Gemini and Replika to impersonate individuals with positions including highly skilled professionals, blue-collar workers, university professors in the humanities, construction workers, computer scientists and hairdressers. The authors frame the work with Bourdieu's work on cultural stratification and distinction.

Results point to sociocultural roots of artificial sociality becoming manifest as habitus in model outputs, partly mediated by infrastructural and design elements. The authors note that research on AI has extensively considered biases related to gender and race, but much less attention has been dedicated to class.
sources:
- peer_reviewed | New Media & Society | https://doi.org/10.1177/14614448251338273 | 2025-10-01
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