# The day before the midterms, wire, left, center and right newsrooms agree AI chatbots are giving voters bad election information — but the error rates they print come from different studies, different question sets and different months. - Canonical: https://thestochasticparrot.com/audits/ai-chatbots-voter-advice - Structured (spans, splits, sources): https://thestochasticparrot.com/audits/ai-chatbots-voter-advice.json - Date: 2026-11-03T01:50:45Z - Context: A coverage brief: how 8 outlets each framed the same news event. - License: CC BY 4.0 --- [ COVERAGE BRIEF // ai-chatbots-voter-advice // 2026-11-02 ] # Chatbots Were Asked Different Voter Questions. The Error Rates Differ Too. ## The day before the midterms, wire, left, center and right newsrooms agree AI chatbots are giving voters bad election information — but the error rates they print come from different studies, different question sets and different months. Eight records landed on this desk for one story: chatbots are answering voters' election questions, and some of the answers are wrong. Every record here touches the hazard. What they do not share is a number, and the reason is not a quarrel — it is a measurement. Each rate was produced by a different research team, on a different question set, against a different definition of error, in a different month. This is a brief, not an audit, and it carries no hard split, because none of these figures can be set against another and made to break. The range runs from five to forty, and the spread is arithmetic, not argument. The AFP wire, carried by France 24, reports Forum AI's November finding: "One recent study by Forum AI, a start-up that evaluates AI models, found that almost 40 percent of chatbots' answers to election-related queries contained at least one factual error." NPR, a day later, carries the IFPG study's bottom line: "Across all questions, 5.1% of chatbot answers carried at least one clear factual error and 10.9% a clear error or misleading claim." Forty is not the rebuttal of five. The AFP piece describes "election-related queries"; NPR's file has IFPG testing seven chatbots against authentic voter questions — projected winners, platforms, candidate comparisons, backgrounds, controversies and voting rules — in October. The same NPR file shows the denominator moving inside a single study. On the narrower terrain: "13.9% of chatbot answers to voting-logistics questions were outdated or inaccurate." The voting-logistics subset runs worse than the all-questions rate from the same researchers, the same month, the same models. If one study's own number nearly triples when you change which questions count, the gap between two studies' numbers needs no intrigue to explain. The Hill, in September, filed ISD's construct, which counts two different things and labels them differently: "Testing both generic and adversarial questions across six AI chatbots, 29 percent contained errors, like misidentifying Election Day dates or showing outdated requirements. In 16 percent of the replies, the chatbot answered correctly but did not include important information that could be useful to a voter, such as key deadlines or ways to prove identity." Sixteen percent is a separate completeness finding: the chatbot answered correctly and the voter still lacked the deadline. The New York Post, in June, carried Forum AI five months before the AFP wire did: "About 30% of all responses contained at least one factual error, according to the startup." Same firm, different audit — every topic from healthcare to foreign affairs — so its figure cannot ride the same axis as the November one. The desk will not average these, rank them, or crown one; a mean across mismatched denominators is a number with no referent, and I decline to manufacture it. What the errors look like, the records render in specimens. France 24 (AFP) carries Forum AI's example: "When Google's Gemini 3.6 Flash was asked about voting in Pennsylvania, the chatbot gave November 3 as the deadline to request a mail-in ballot. The actual deadline was a week earlier." NPR carries IFPG's per-model spread: "On voting questions, Claude gave wrong or outdated information one in three times, Copilot one in four and ChatGPT one in 10, according to the IFPG report." One in three is not one in ten; NPR relays that IFPG found accuracy depended more on which chatbot than on which question. The error rate is not a property of chatbots in general. It is a property of which one a voter opened. Usage is a separate shelf, and every figure on it is a survey estimate — what respondents said, not what anyone measured them doing. The Washington Sun: "More than 60% of voters already report using AI tools at least a few times a month, and nearly a quarter of voters use the technology daily" — attributed to an AAPC Foundation study. Newsmax, relaying Reuters, has Hickey's estimate: "As online election information has surged, some 25% of eligible voters have asked a chatbot about a political candidate when deciding how to vote, according to Cameron Hickey, CEO of the National Conference on Citizenship." USA TODAY prints the small end: "About 10% of Americans report using chatbots to get their news, according to the Institute for Strategic Dialogue." NPR, citing Pew, lands between: "About half of American adults reported using AI-powered chatbots and about 42% of users said they use them to search for information." Sixty, 25, 10, 42 — four denominators, four wordings, and each record prints its attribution in the sentence. The rates do not need reconciling. They were never measuring the same act. The emphasis split runs along a lean seam, and it is a framing split, not a factual one. The center and wire records lead with accuracy and usage. Fox News, in August, adds a second frame — that the chatbots themselves lean — on a Just Facts study: "The study found ChatGPT correctly answered 94% of questions designed to elicit falsehoods from the right, compared with 75% from the left." That is one study's result, reported by one outlet; the study itself cautioned, per Fox's own file, that its precisely worded questions may have handed the chatbots roadmaps to the correct answers. The New York Post's June file, for its part, is in this corpus for one exhibited figure — the 30 percent factual-error rate, attributed to Forum AI — and no lean claim from it is set against Fox's here. The Washington Sun stays on the sourcing beat: "Some 18% of the chatbot’s September answers cited one of these sites, up from 7% in August." Its own quoted researcher says the concern is reach without validation, not overt bias in the sites. One characterization in the Washington Sun file deserves a label. A researcher there describes the systems as "big black boxes with a whole bunch of parts that they don’t tell us about." That is a description of the systems by a source, printed as such; it is a characterization, not a finding in the file, and the file itself notes the researchers could study only what the chatbots cited, not why. Two facts about the sample, stated once. AFP reaches this corpus once, through France 24; Reuters once, through Newsmax — each is one report printed by one carrier, and neither counts as a second confirmation of anything. The right-leaning bucket holds three records here against NPR's one on the left; that is the composition the frozen corpus happened to deliver, and it describes these files, not any desk's judgment about the story. No claim about who skipped what follows from it. What the records establish: chatbots answered election questions with errors, at rates that range from 5.1% to almost 40 percent depending on what was asked, of which models, and when — and each rate is bound to its own study in the sentence that carries it. What no record establishes: a single correct figure, a settled partisan direction, or a study rebutting another study. Claim: one true error rate exists across these studies · status: unresolved · confidence: the spans examined give it no support; the denominators were built different on purpose. Exhibit lines: 14. ── SHARED: how-often-the-error-rate-range · census ── France 24 (AFP wire): "One recent study by Forum AI, a start-up that evaluates AI models, found that almost 40 percent of chatbots' answers to election-related queries contained at least one factual error." NPR: "Across all questions, 5.1% of chatbot answers carried at least one clear factual error and 10.9% a clear error or misleading claim." NPR: "13.9% of chatbot answers to voting-logistics questions were outdated or inaccurate." The Hill: "Testing both generic and adversarial questions across six AI chatbots, 29 percent contained errors, like misidentifying Election Day dates or showing outdated requirements. In 16 percent of the replies, the chatbot answered correctly but did not include important information that could be useful to a voter, such as key deadlines or ways to prove identity." New York Post: "About 30% of all responses contained at least one factual error, according to the startup." ── SHARED: what-the-errors-look-like · census ── France 24 (AFP wire): "When Google's Gemini 3.6 Flash was asked about voting in Pennsylvania, the chatbot gave November 3 as the deadline to request a mail-in ballot. The actual deadline was a week earlier." NPR: "On voting questions, Claude gave wrong or outdated information one in three times, Copilot one in four and ChatGPT one in 10, according to the IFPG report." ── SHARED: who-is-using-them · census ── The Washington Sun: "More than 60% of voters already report using AI tools at least a few times a month, and nearly a quarter of voters use the technology daily" Newsmax (Reuters wire): "As online election information has surged, some 25% of eligible voters have asked a chatbot about a political candidate when deciding how to vote, according to Cameron Hickey, CEO of the National Conference on Citizenship." USA TODAY: "About 10% of Americans report using chatbots to get their news, according to the Institute for Strategic Dialogue." NPR: "About half of American adults reported using AI-powered chatbots and about 42% of users said they use them to search for information" ── FRAMING: partisan-lean · framing ── Fox News: "The study found ChatGPT correctly answered 94% of questions designed to elicit falsehoods from the right, compared with 75% from the left." ── SHARED: source-quality · census ── Fox News: "Overall, the study found that just 46% of the sources cited by the chatbots were valid." The Washington Sun: "Some 18% of the chatbot’s September answers cited one of these sites, up from 7% in August." ── READ: France 24 (AFP wire) · the hazard stated at the top ── anchor: "One recent study by Forum AI, a start-up that evaluates AI models, found that almost 40 percent of chatbots' answers to election-related queries contained at least one factual error." objective: built to open on the highest-severity framing — "sowing information chaos" — and to carry the Forum AI error rate as the lead number, with the constructive findings lower in the file. arrangement: the 40-percent figure and the Pennsylvania specimen sit above the fold; the Brennan Center's pushback findings arrive last. confidence: strong ── READ: NPR · per-model spread and the denominator kept visible ── anchor: "Across all questions, 5.1% of chatbot answers carried at least one clear factual error and 10.9% a clear error or misleading claim." objective: built to print the IFPG rates with their denominators in the same sentence and to sort error by chatbot, with vendor rebuttals carried on the record. confidence: strong ── READ: The Hill · incompleteness filed separately from error ── anchor: "In 16 percent of the replies, the chatbot answered correctly but did not include important information that could be useful to a voter, such as key deadlines or ways to prove identity." objective: built to relay ISD's two constructs as two constructs — wrong answers and thin ones — inside a usage-and-trust feature rather than an alarm piece. confidence: strong ── READ: New York Post · the body's number and the headline's ── anchor: "About 30% of all responses contained at least one factual error, according to the startup." objective: built to carry Forum AI's June audit with its neutrality and state-media findings intact, under a headline whose "90%" covers a broader composite of flaws than the body's factual-error rate. arrangement: the body's opening glosses the 90% as a composite of factual errors, bias and state-media citation; the exhibit's 30% is the factual-error rate alone. confidence: tentative ── READ: Fox News · the lean frame, sourced to one study ── anchor: "The study found ChatGPT correctly answered 94% of questions designed to elicit falsehoods from the right, compared with 75% from the left." objective: built to seat the partisan-lean finding in the lead and to carry Just Facts' source-validity results alongside the study's own self-caution about its precisely worded questions. arrangement: the study's caution about its question design runs below the lead; a reader who stops early gets the asymmetry without the limitation. confidence: tentative ── READ: The Washington Sun · the citation supply chain ── anchor: "Some 18% of the chatbot’s September answers cited one of these sites, up from 7% in August." objective: built to document where ChatGPT's election answers are sourced from — slop sites up, Ballotpedia blocked by a crawler rule — with the mechanical explanation on the record. confidence: strong ── READ: Newsmax (Reuters wire) · campaigns courting the models ── anchor: "some 25% of eligible voters have asked a chatbot about a political candidate when deciding how to vote, according to Cameron Hickey, CEO of the National Conference on Citizenship" objective: built to carry the Reuters file on generative engine optimization, with the usage estimate attributed inside the sentence and a researcher's misinformation caution included. confidence: strong ── READ: USA TODAY · the small share, with the growth hedge ── anchor: "About 10% of Americans report using chatbots to get their news, according to the Institute for Strategic Dialogue." objective: built to print the low-end usage figure with its attribution and to carry ISD's projection that the share is likely to grow, alongside MIT's finding that answers vary by user. confidence: strong ## Sources - France 24 (AFP wire) — "Spewing US election falsehoods, AI chatbots face scrutiny": https://www.france24.com/en/live-news/20261101-spewing-us-election-falsehoods-ai-chatbots-face-scrutiny - NPR — "AI chatbots are becoming political advisers. Their answers can mislead voters": https://www.npr.org/2026/11/02/nx-s1-6025407/chatbots-midterms-voting-advice - The Hill — "Voters are turning to AI on politics. Is it up to the task?": https://thehill.com/policy/technology/6104694-ai-reshaping-voter-information/ - New York Post — "90% of AI chatbot answers about midterm elections are flawed, stunning analysis shows": https://nypost.com/2026/06/05/business/91-of-ai-chatbot-answers-about-midterm-elections-are-flawed-stunning-analysis-shows/ - The Washington Sun — "Mysterious New Websites Are Showing Up in AI’s Midterms Answers": https://www.washingtonsun.com/campaigns/ai-chatbot-election-information-mysterious-websites - Newsmax — "US Political Candidates Tailor Messages for AI Models Used by Voters": https://www.newsmax.com/politics/elections-chatbots-campaigns/2026/10/26/id/1273994/ - USA TODAY — "Research suggests some voters using AI to help with midterms information": https://www.usatoday.com/story/news/politics/elections/2026/09/19/research-ai-aritificial-intelligence-politics-information-2026-midterms/91808094007/ - Fox News — "Alarming study finds top chatbots more likely to repeat falsehoods from the left: 'Truly astonishing'": https://www.foxnews.com/politics/alarming-study-finds-top-chatbots-more-likely-repeat-falsehoods-left-truly-astonishing