Auditing Election-Disinformation Compliance in Open-Weight LLMs Across African and Western Contexts
Year: 2026
Type: preprint
Abstract: This study tests how often open-weight AI models agree to write false election information such as a wrong voting date when asked. We tested 220 prompts across four elections: South Africa, Kenya, the UK, and the US (Pennsylvania). Models complied about 97% of the time with no safety measures. A short safety instruction (Constitution) reduced this to about 12%, but also made models refuse some genuine election questions. Code, prompt templates (with false claims replaced by placeholders), judgement labels, and results are included. Raw model outputs and filled-in false claims are withheld to prevent misuse.
Authors: Joshua David Padoa, Karan Singh, Atharva Gupta
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7166438773
Cites:
Cited by:
Related to: