Robustness and Confounders in the Demographic Alignment of LLMs with Human Perceptions of Offensiveness
This work addresses the problem of robust bias evaluation in LLMs for researchers and practitioners, highlighting the need for multi-dataset and confounder-aware approaches, though it is incremental in refining existing methodologies.
The study examined how demographic biases in large language models align with human perceptions of offensiveness across five datasets with 220K annotations, finding that demographic traits like race inconsistently affect alignment and are often overshadowed by confounders such as document difficulty and annotator sensitivity.
Large language models (LLMs) are known to exhibit demographic biases, yet few studies systematically evaluate these biases across multiple datasets or account for confounding factors. In this work, we examine LLM alignment with human annotations in five offensive language datasets, comprising approximately 220K annotations. Our findings reveal that while demographic traits, particularly race, influence alignment, these effects are inconsistent across datasets and often entangled with other factors. Confounders -- such as document difficulty, annotator sensitivity, and within-group agreement -- account for more variation in alignment patterns than demographic traits alone. Specifically, alignment increases with higher annotator sensitivity and group agreement, while greater document difficulty corresponds to reduced alignment. Our results underscore the importance of multi-dataset analyses and confounder-aware methodologies in developing robust measures of demographic bias in LLMs.