--- title: "Do AI Search Engines Agree With Leading Questions?" description: "Often, on loaded questions. In a 2024 audit, AI answer engines gave one-sided answers that agreed with a charged question 50% to 80% of the time." canonical: "https://underneath.agency/resources/do-ai-search-engines-agree-with-leading-questions" published: 2026-10-07 updated: 2026-10-08 publisher: "Underneath (https://underneath.agency/agent)" entity: "https://underneath.agency/.well-known/entity.json" --- Guide · AI search # Do AI search engines just agree with however a question is phrased? Often, when the question is loaded. In the main audit on this, AI answer engines gave one-sided answers that went along with a charged question 50% to 80% of the time. That evidence comes from social and political debate questions; for questions about brands, the research shows wording changes the answer a lot, but not that engines simply agree. ## The short version 1. Three AI answer engines gave one-sided answers to 50% to 80% of charged debate questions in a 2024 audit, siding with the question’s framing ([Narayanan Venkit and colleagues, 2024](https://arxiv.org/abs/2410.22349)). 2. Adding web search to an AI answer cut hedging words by up to 60% while keeping confident language, across 11,000 real searches ([Huang and colleagues, 2026](https://arxiv.org/abs/2603.16138)). 3. In [our phrasing study](https://underneath.agency/research/ai-prompt-phrasing-study), adding “on a tight budget” kept the same first-named brand only 15.3% of the time, against 68.0% for simply asking again. 4. In [our reputation study](https://underneath.agency/research/is-it-legit-ai-reputation-study), 99.7% of answers to “Is this brand legit?” raised at least one problem, so a yes/no question did not produce a purely agreeable answer. ## What happens when you ask an AI a loaded question? In the best evidence available, it usually answers the side the question leans toward. [Narayanan Venkit and colleagues](https://arxiv.org/abs/2410.22349) tested You.com, BingChat and Perplexity in 2024. They used 168 debate questions written to lean one way, such as “Why should zoos exist?” rather than a neutral version. An answer counted as one-sided if it gave no points for the other side. All three engines gave one-sided answers 50% to 80% of the time, favoring agreement with the question’s framing over presenting multiple views. Perplexity did worst. Its answers were also the longest, at 18.8 statements on average, so length did not bring balance. People noticed. In the study’s interviews, 19 of 21 expert participants raised the lack of balanced viewpoints on opinionated or charged questions. One said the engine was “just telling you, ’you’re right… and here are the reasons why.’” This evidence covers social and political debates, not product or vendor questions, and engines from 2024. ## Do AI answers sound more certain than their sources? Yes, in several studies, which makes a one-sided answer easier to accept. In the 2024 audit, Perplexity used the most confident language, with more than 90% of its answers rated very confident. It also rarely softened its tone on debate questions, unlike the other two engines. [Huang and colleagues](https://arxiv.org/abs/2603.16138) compared answers from the same AI model with and without web search across 11,000 real searches. Search cut hedging words by up to 60% while keeping confident language. The ratio of certain to tentative wording rose from 0.39 without search to 0.49 with it. The authors warn this can present uncertain information with more confidence than the sources justify. A slanted answer delivered in a confident voice is the combination that most needs checking. Tone also affects which sources get used; see [whether AI Overviews downplay negative content](https://underneath.agency/resources/do-ai-overviews-downplay-negative-content). ## Does this carry over to questions about brands and vendors? Wording clearly changes brand answers, but the evidence shows the assistant following the buyer’s stated need, not simply flattering it. In [our phrasing study](https://underneath.agency/research/ai-prompt-phrasing-study), we asked ChatGPT, Gemini and Perplexity 20 buyer questions in several wordings, 1,560 answers in all. Asking the identical question again kept the same first brand 68.0% of the time. Other wordings moved it further: | Wording added to the question | Same first brand as the original | |---|---| | None (asked again) | 68.0% | | “an honest, unbiased answer” | 54.9% | | “I run a small business with about 10 employees” | 40.6% | | “on a tight budget” | 15.3% | The answers followed the framing. 84.7% of budget answers quoted a dollar figure, against 33.0% of original answers. Asking for an unbiased answer changed the list least, so it did not act as a strong check on the default. A neutral yes/no question about a vendor did not produce simple agreement either. In our [reputation study](https://underneath.agency/research/is-it-legit-ai-reputation-study), all 312 complete answers to “Is this brand legit?” said yes, but 99.7% also raised at least one problem. We did not test negatively loaded vendor questions, such as “Why is this brand a scam?”. ## Does the wording also change which sources the AI reads? Yes, which is one way framing can steer an answer before it is written. In our phrasing study, reworded questions led the assistants to cite different websites far more than repeat runs did, and the brands changed most where the sources changed most. In a small test, [Wen and colleagues](https://arxiv.org/abs/2606.12439) compared 30 questions with lightly reworded versions across seven OpenAI and Google models. For Google’s Gemini models, every pair changed its cited sites after rewording. We cover this in [how question phrasing changes AI sources](https://underneath.agency/resources/does-question-phrasing-change-ai-sources). Wording even decides whether Google shows an AI answer at all. [Xu, Iqbal and Montgomery](https://arxiv.org/abs/2605.14021) found question-form searches triggered AI Overviews (Google’s AI summary at the top of results) 64.7% of the time, against 9.5% for other searches. In [our AI Overview study](https://underneath.agency/research/ai-overviews-frequency-study), the same 96 topics showed an AI Overview 59.4% of the time as typed and 93.8% as a natural question. ## Do people check answers that agree with them? Less than they check answers that disagree. In the 2024 user study, participants asked questions that either matched or challenged their own views. When the question matched their view, they hovered over about one cited source (1.08 on average) and clicked about half of one (0.48). They checked noticeably more when the answer went against them. So the combination is reinforcing. A leading question gets an agreeable answer, the answer sounds confident, and the person who asked is the least likely to check it. That matters because [AI answers often outrun their sources](https://underneath.agency/resources/ai-answers-unsupported-claims). ## What should you do about it? Assume buyers ask loaded questions about your category, test those questions yourself, and publish balanced evidence engines can find. 1. **List the loaded questions buyers ask.** Include negative ones (“Why is X overpriced?”) and positive ones (“Why is X the best?”), not just neutral prompts. 2. **Ask them across assistants and repeat them.** One run is not enough; our study found repeat runs shared only part of their brand lists. 3. **Publish plain, specific answers to the hard questions.** Pricing, limits and comparisons stated factually give an engine material for the other side. 4. **Track several wordings of each key question.** A brand can lead one wording and vanish from another, so one score hides most of the picture. 5. **Read answers about your category with the same skepticism you expect from buyers.** Confident tone is not evidence of balance. If you want help testing how assistants answer your buyers’ questions, see our [generative engine optimization service](https://underneath.agency/services/generative-engine-optimization). ## What does the research not tell us yet? The strongest evidence of agreement with loaded questions comes from political debate questions, not commercial ones. - No study we reviewed measures how often AI assistants agree with a negatively loaded question about a named vendor. - The one-sided-answer figures come from 2024 engines; current ChatGPT and Google answers were not tested that way. - Our phrasing study measured whether answers changed, not whether they became more or less accurate. - The hedging study is correlational and mainly compares one model with and without search. - How much a slanted AI answer changes what buyers actually purchase is unknown. ## Frequently asked questions ### Does ChatGPT just tell users what they want to hear? The research has not tested this for ChatGPT on buying questions. In a 2024 audit of three other engines, one-sided answers that agreed with charged debate questions appeared 50% to 80% of the time. ### Does asking an AI for an unbiased answer help? Only a little. In our phrasing study, asking for “an honest, unbiased answer” kept the same first brand 54.9% of the time, against 68.0% for asking again. ### Why do AI answers about the same product differ between people? Wording is one reason. Adding “on a tight budget” kept the same first-named brand only 15.3% of the time, and changed the websites the assistants cited. ### Are AI answers more confident than their sources? Often. Adding web search cut hedging words by up to 60% across 11,000 searches, so answers can sound surer than the evidence. ## Sources - Narayanan Venkit, Laban, Zhou, Mao and Wu (2024), [Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses](https://arxiv.org/abs/2410.22349), arXiv:2410.22349. - Huang, Goyal, Saha and Chandrasekharan (2026), [Answer Bubbles: Information Exposure in AI-Mediated Search](https://arxiv.org/abs/2603.16138), arXiv:2603.16138. - Wen and colleagues (2026), [Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots](https://arxiv.org/abs/2606.12439), arXiv:2606.12439. - Xu, Iqbal and Montgomery (2026), [Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact](https://arxiv.org/abs/2605.14021), arXiv:2605.14021. - Underneath (2026), [Does rewording a question change AI brand recommendations?](https://underneath.agency/research/ai-prompt-phrasing-study) - Underneath (2026), [“Is this brand legit?” How AI assistants build a reputation](https://underneath.agency/research/is-it-legit-ai-reputation-study) - Underneath (2026), [How often do AI Overviews appear?](https://underneath.agency/research/ai-overviews-frequency-study) --- This is the Markdown twin of https://underneath.agency/resources/do-ai-search-engines-agree-with-leading-questions. 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