Year: 2026
Type: conference-paper
Abstract: Large language models are increasingly deployed in multilingual contexts, yet safety alignment and bias evaluation remain overwhelmingly English-centric.We investigate whether social biases generalise across languages by submitting 4,900 symmetric English-Swahili prompt pairs to GPT-5.2 and Gemini 2.5 Flash across nine demographic bias axes, yielding 19,600 completions evaluated for stereotype prevalence, sentiment, refusal behaviour, and cross-lingual semantic similarity.Our findings show that bias transforms rather than transfers: stereotype rates shifted by up to 12 percentage points on specific axes, Gemini's neutral-sentiment rate doubled in Swahili, and GPT-5.2 refused 169 prompts in English and zero in Swahili, consistent with refusal behaviour anchored to English-language surface forms at the behavioural level.Over 55% of prompt pairs produced semantically dissimilar completions across both models.These reinforce the idea that English-only bias audits do not produce adequate coverage for multilingual deployment.
Authors: Ruolei Zhang, Teddy Njuguna, Yue Feng
Institution: University of Birmingham
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: gold
OpenAlex ID: W7166889873
FWCI:
Cites:
Cited by:
Related to: