Real-time accent-altering technology: The message is clear, and it is dehumanizing

Payne, Ameena; Bauler, Clara Vaz; Austin, Tasha; Clemons, Aris Moreno

doi:10.31234/osf.io/u4p2j

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The film uses this play on spoken language, social identity, and power to highlight the concept of hegemony, defined by Gramsci Gramsci (1992) as the cultural norms that uphold structures of power and domination. While fictional, current technologies can now manipulate voices to sound like specific individuals or reduce accents by replacing them with more "normalized" speech Payne et al (2024). How these dynamics compare with written language is less known.…”

Section: Introductionmentioning

confidence: 99%

Large Language Models, Social Demography, and Hegemony: Comparing Authorship in Human and Synthetic Text

Alvero,

Lee,

Regla-Vargas

et al. 2024

Preprint

View full text Add to dashboard Cite

Large language models have become extremely popular over a short period of time given their ability to generate text that resembles human writing across a variety of domains and tasks. This popularity and breadth of use also put this technology at hazard to fundamentally reshape how written language is perceived and evaluated. Spoken language has long played a role in maintaining power and hegemony in society, especially through ideas of social identity and ``correct'' forms of language. But as human communication becomes even more reliant on text and writing, it is important to understand how these processes might shift and who is more likely to see their writing styles reflected back at them through modern AI. We therefore ask the following question: who does generative AI write like? To answer this, we compare writing style features in over 150,000 college admissions essays submitted to a large public university system and an engineering program at an elite private university with a corpus of over 25,000 essays generated with GPT-3.5 and GPT-4 to the same writing prompts. For individual writing style features, we find that humans exhibit more variability than generative AI. When comparing all of the writing features, we find that the AI-generated essays are most similar to essays submitted by students with higher levels of social privilege. Future studies using text data and large language models might consider human and AI authorship characteristics and evaluation of writing.

show abstract

Section: Introductionmentioning

confidence: 99%