Search engines (SEs) and large language models (LLMs) are central to political information access, yet their algorithmic decisions and potential underlying biases remain underexplored. We developed a standardized, privacy-preserving, bot-and-proxy methodology to audit four SEs and two LLMs before the 2024 European Parliament and US presidential elections. We collected answers to approximately 4,360 queries related to elections in five EU countries and 15 US counties, identified political entities and topics in those answers, and mapped them to ideological positions (EU) or issue associations (US). In Europe, SE results disproportionately mentioned far-right entities beyond levels expected from polls, past elections, or media salience. In the US, Google strongly favored topics more important to Republican voters, while other search engines favored issues more relevant to Democrats. LLMs responses were more balanced, although there is evidence of overrepresentation of far-right (and Green) entities. These results show evidence of bias and open important discussions on how even small skews in widely used platforms may influence democratic processes, calling for systematic audits of their outputs.

翻译：搜索引擎和大型语言模型是获取政治信息的关键渠道，但其算法决策及潜在偏见仍未得到充分研究。我们采用标准化的、保护隐私的自动化程序与代理方法，在2024年欧洲议会选举和美国总统大选前，对四种搜索引擎和两种大型语言模型进行了审计。针对五个欧盟国家和十五个美国县的选举相关议题，我们收集了约4360个查询的答案，识别其中的政治实体与话题，并将其映射至意识形态立场（欧盟）或议题关联（美国）。在欧洲，搜索结果中提及极右翼实体的比例远超民意调查、历史选举结果或媒体报道的预期水平；在美国，谷歌明显偏向共和党选民更关注的议题，而其他搜索引擎则更倾向民主党选民关注的议题。大型语言模型的回答更为均衡，但仍存在极右翼（及绿党）实体被过度呈现的证据。这些结果揭示了偏见的客观存在，并开启了重要讨论：即便是广泛使用的平台中微小偏差，也可能影响民主进程，亟需对其输出进行系统性审计。