Large Language Models Are Biased Because They Are Large Language Models
Year: 2025
Type: article
Abstract: This position paper’s primary goal is to provoke thoughtful discussion about the relationship between bias and fundamental properties of large language models (LLMs). I do this by seeking to convince the reader that harmful biases are an inevitable consequence arising from the design of any large language model as LLMs are currently formulated. To the extent that this is true, it suggests that the problem of harmful bias cannot be properly addressed without a serious reconsideration of AI driven by LLMs, going back to the foundational assumptions underlying their design.
Source: Computational Linguistics
DOI: 10.1162/coli_a_00558
Author: Philip Resnik
Institution: University of Maryland, College Park
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4408929481