Can AI Debias the News? LLM Interventions Improve Cross-Partisan Receptivity but LLMs Overestimate Their Own Effectiveness
Partisan news media erode cross-partisan trust, but large language models (LLMs) offer the potential of debiasing such content at scale. Across two pre-registered experiments, we tested whether LLM-generated debiasing of liberal news headlines improves conservative readers' trust-relevant judgments. In Study 1, subtle lexical debiasing (replacing emotive words with moderate synonyms) had no effect on any outcome. Study 2 found that a more substantive reframing intervention significantly increased conservatives' perceived trustworthiness, completeness, and willingness to engage with liberal news headlines, without producing a backfire effect among liberals. In Study 1, the intervention pro...
| Authors | Faisal Feroz, Jonas R. Kunst |
|---|---|
| Primary category | cs.CL |
| Published | 1 May 2026 |
| Last updated at source | 24 July 2026 |
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