Which? AI chatbots give risky consumer advice; reliability gaps

Meta‑facing chatbots that many people treat like quick advisers are still giving unsafe, sometimes dangerously misleading guidance on legal, financial and consumer‑rights questions — and the gap between conversational fluency and factual reliability is wide enough to matter for everyday Windows users, IT teams and enterprise risk managers alike. A new consumer‑facing test from the UK finds that mainstream assistants — including Meta AI and OpenAI’s ChatGPT — returned inaccurate, unclear or at times risky advice across a set of 40 realistic consumer queries, with lesser‑known players such as Perplexity outperforming some household names on raw reliability scores.

Background / Overview​

The consumer group Which? ran a controlled evaluation of six widely used AI chatbots, prompting each with the same battery of everyday questions spanning personal finance, legal entitlements, consumer rights, travel and health. Experts then scored replies for accuracy, clarity, usefulness, relevance and ethical responsibility. The test was designed to mirror the exact kinds of queries non‑expert users bring to chatbots in the wild — the kind of lightweight triage that often precedes a decision with real financial or legal consequences. The headlines are simple but important: Perplexity achieved the highest reliability score in this round at roughly 71%, while Meta AI performed worst at 55%, and ChatGPT scored 64%, placing it near the lower half of the pack. These rankings were driven by measurable failures: wrong numeric thresholds, unsafe or oversimplified legal advice, and citations or links that pointed users toward questionable third‑party services. This is not an isolated alarm. The Which? findings sit alongside a broader set of independent audits and newsroom tests (including large projects coordinated by public broadcasters) that show conversational AI is still prone to hallucination, sourcing failures and answer‑first overconfidence — a dangerous combination when the user expects actionable guidance.

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The Which? test: scope, methodology and what was measured​

What was asked, and how the answers were scored​

Which? put 40 consumer‑oriented prompts to six AI systems: ChatGPT (OpenAI), Google Gemini (and Gemini AIO), Microsoft Copilot, Meta AI and Perplexity. The prompts were realistic, jurisdiction‑sensitive scenarios — for example, UK‑specific tax and ISA questions, flight cancellation and refund questions, and consumer rights disputes. Replies were judged by experts on a five‑axis rubric that privileged practical correctness and ethical responsibility rather than style alone. This approach matters because it measures real‑world risk, not just benchmarked language fluency. An AI that writes beautifully but tells a user to exceed a statutory tax allowance or to forgo a claim can do measurable harm — and that’s the precise set of failures Which? flagged.

Strengths and limits of the test​

• Strengths:
• Realistic prompts mirror consumer behaviour.
• Expert human scoring captures nuance and real‑world consequences.
• Multiple assistants tested under identical conditions for direct comparison.
• Limits:
• Tests are a snapshot in time: model updates, live‑web access, and vendor patches can change behaviour quickly.
• Results depend on the exact phrasing of prompts; different phrasing can trigger better or worse performance.
• Some claims about user numbers or trust were taken from surveys and press reporting that vary by methodology; those specific figures should be treated cautiously pending primary data.

Where precise details could not be independently corroborated from multiple public records, the report explicitly flagged those points for caution — a sensible editorial practice mirrored in the wider investigative coverage.

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Key findings: what the chatbots got wrong​

Notable misadvice with real consequences​

• ISA allowance error — Several models accepted a deliberately incorrect user premise that the UK ISA (Individual Savings Account) annual allowance was £25,000 and proceeded to give advice based on that inflated figure, rather than flagging the error; the correct allowance for the tested tax year was £20,000. This is not a trivial mistake: acting on incorrect tax thresholds can produce regulatory repercussions with HM Revenue & Customs.
• Unsafe travel and consumer rights guidance — Some assistants offered blanket statements — for example, asserting passengers were always entitled to full refunds after a cancelled flight — while failing to explain the conditional nature of ticket types, alternative rerouting options, timing and exceptional circumstances that significantly alter legal remedies. That kind of oversimplification risks steering users away from practical remedies that may be available.
• Questionable source links — In multiple cases the chatbots cited weak or non‑authoritative sources such as forum threads or outdated posts. Which? found examples where an assistant referenced a three‑year‑old Reddit thread to justify timing advice on booking flights — a fragile provenance strategy for decisions involving money. Worse, some replies surfaced links to dubious tax reclaim services that charge high fees and operate with aggressive marketing practices, introducing a genuine consumer‑protection risk.
• Health and harm — Though Which? focused on consumer topics, the tests echoed broader findings in the sector: AI assistants sometimes contradict public‑health guidance or provide answers that omit crucial safety caveats, which can be dangerous when users treat chatbots as first‑line medical sources. Broader audits have documented similar issues across multiple platforms.

Comparative performance — the ranking that matters​

• Perplexity: highest reliability (~71%).
• Google AIO: ~70%.
• Google Gemini standard: ~69%.
• Microsoft Copilot: ~68%.
• ChatGPT (OpenAI): ~64%.
• Meta AI: lowest at ~55%.

These headline numbers compress a matrix of domain‑specific strengths and weaknesses: a model that scored well on factual sourcing might still fail on legal nuance, and a chatty assistant might produce a useful summary while omitting a crucial statutory exception. Nevertheless, the ranking is a clear signal: brand familiarity and market share do not guarantee superior reliability in consumer advice tasks.

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Why assistants fail: the technical anatomy of risk​

Hallucinations, retrieval flaws and sycophancy​

• Hallucinations: models sometimes generate plausible‑sounding but false facts — inventing figures, misdating events, or fabricating attributions. That can happen even when a model tries to ground an answer in retrieved content, because the generative step can synthesize narrative detail not present in the sources.
• Retrieval failures / poor provenance: when the web retrieval layer surfaces low‑quality or manipulated pages, the model may summarize or amplify that content with confident language, creating the impression of authority where none exists. Public tests have repeatedly shown weak citation hygiene to be a dominant failure mode.
• Sycophancy / premise acceptance: models often accept user‑provided premises rather than challenge clearly incorrect inputs. In settings where the user supplies a wrong numeric threshold (like the ISA example), an assistant tuned for helpfulness can compound the error by producing a full plan based on the bad premise. This design trade‑off — prioritising conversational continuity and perceived helpfulness — produces real downstream risk.

Design trade‑offs: responsiveness vs. caution​

Vendors tune assistants for different balances: lower refusal rates and more assertive answers boost engagement but increase the chance the model will respond confidently to risky prompts. Conversely, aggressive refusal behavior reduces harm but frustrates users. The Which? results and other audits show this is a live product design trade‑off with regulatory and legal implications.

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What this means for consumers — practical, non‑technical advice​

AI chatbots offer convenience for quick triage, drafting and comparative research, but they are not a substitute for authoritative sources or licensed professionals when stakes are high.

• Treat AI as a research assistant, not an adviser: use chatbots to compile a checklist, identify possible routes, or draft a query to a human expert, but do not rely on them to make the final decision on legal, tax or medical matters.
• Demand sources and verify: ask the assistant to provide timestamped links to primary authorities (HMRC, NHS, regulator pages). If the model cites forums or outdated pages, treat that as a red flag. Confirm the information on official government or regulator sites before acting.
• Cross‑check numeric thresholds: when an answer depends on a number (tax limits, statutory deadlines), cross‑verify those numbers directly on authoritative portals rather than trusting the model’s phrasing.
• Keep an audit trail: save important exchanges and the listed sources. That record can be invaluable if a consumer has to dispute a service or explain why a decision was made.

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What this means for Windows users, IT teams and enterprise defenders​

Enterprise risk: the consumer problem becomes a corporate one​

• Employees will use AI assistants for quick legal or financial triage, expense queries, vendor negotiations and even basic troubleshooting. Mistakes here can be costly: misinterpreted procurement rules, incorrect compliance steps, or bad contract language drafted from a hallucinated clause can create legal exposure.
• Enterprises must assume that popular consumer models will be part of the operational fabric — either via sanctioned tools integrated into workflows or through shadow usage on personal devices and browsers. This is an enterprise governance issue, not just a consumer education challenge.

Practical controls and policies for IT and security teams​

• Policy first, then tools
• Define clear usage policies for AI assistants in the workplace. Specify permitted use cases and forbidden actions (e.g., do not use consumer chatbots to draft legally binding contracts, or to make tax filings). 1.
• Prefer tools with transparent citation modes
• When choosing vendor integrations, prioritise offerings that return explicit, timestamped citations and that support a verifiable retrieval provenance. Require suppliers to document the retrieval and grounding stack and SLA for model updates.
• Network controls and monitoring
• Use content‑filtering policies to flag or block outbound traffic to risky third‑party services surfaced by chatbots (for example, suspicious tax reclaim websites). Log AI interactions on enterprise machines so security teams can audit incident chains. 1.
• Endpoint and identity hygiene
• Prevent the uploading of sensitive or regulated data to public chatbots via DLP and endpoint protections. Enforce single‑sign‑on and corporate identity controls for any AI services used on corporate accounts.
• Human‑in‑the‑loop gating for high‑risk tasks
• Any AI‑assisted work that touches legal, tax, finance or safety must include mandatory human review and sign‑off by designated professionals.
• Contractual protections with vendors
• For API or enterprise contracts, insist on non‑training clauses (to avoid models absorbing sensitive data), incident reporting, indemnities for faulty outputs when vendor documentation claims domain accuracy, and transparency about safety testing. 1.
• Training and simulated red‑team exercises
• Run tabletop exercises where employees bring AI‑sourced advice to a review panel; practice catching and correcting synthetic hallucinations and misleading citations. This builds institutional muscle memory.

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Regulatory, legal and vendor responses — what to watch next​

Regulators in the UK and beyond are watching this space closely. Consumer groups, national regulators and public broadcasters have all published audits that stress common failure modes, and producers are being pushed toward clearer provenance, refusal heuristics and better safety ergonomics. Which?’s report adds consumer protection urgency to that agenda by demonstrating concrete consumer‑level harms and vulnerabilities.
Vendors’ replies vary. Some emphasise feature improvements (citation modes, web‑connected research modes, improved refusal behaviour), while others point to the need for consumer education and professional backup for high‑stakes tasks. Those responses are necessary but insufficient: the Which? results show product changes and public education must happen in parallel. Legal exposure is also rising. Plaintiffs have begun pursuing claims related to emotional harm and other serious outcomes tied to chatbots’ behaviour; while causation is complex, those suits signal that courts will scrutinise product design choices such as memory defaults, refusal policies and crisis escalation procedures. Enterprises contracting with vendors should consider the potential for third‑party litigation if AI outputs are used operationally without proper controls.

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Vendor differences and how to choose an assistant​

Not all chatbots are the same. The Which? test demonstrates that smaller or research‑oriented products can outperform bigger, more widely distributed assistants on specific trust metrics — notably citation behaviour and accuracy on narrow consumer tasks. That suggests procurement decisions should be evidence‑based, not brand‑based.

• Evaluate on the dimensions that matter: verifiable citations, timestamping, real‑time web access vs static model cutoff, and documented safety testing.
• Require vendors to disclose retrieval sources and to provide an auditable trail for high‑stakes queries.
• Prioritise models that offer conservative defaults for legal, financial and health topics, and that provide clear human‑escalation paths.

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Practical checklist for WindowsForum readers (consumers and IT pros)​

• For consumers:
• Double‑check any statutory numbers on official government sites.
• Don’t pay for third‑party reclaim services unless you’ve verified their regulator status.
• Save AI replies and cross‑check the cited sources before acting.
• For IT and security teams:
• Audit shadow AI usage across endpoints and browsers.
• Implement DLP rules to stop sensitive data exfiltration to public chatbots.
• Prefer enterprise AI products with explicit citation logs.
• Train staff to require human sign‑off on legal/financial decisions.
• Add contractual protections in vendor agreements (incident reporting, indemnities, non‑training clauses).

These steps reduce risk while preserving the productivity gains conversational AI can deliver. They also map directly to governance actions that will be useful in regulatory reviews and insurance negotiations.

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Strengths, risks and the sober verdict​

The Which? test and allied audits make three things clear:

• Strengths: AI chatbots offer fast, accessible summaries and can dramatically speed triage, drafting and basic research. They can democratise access to information when used correctly and paired with authoritative sources.
• Persistent risks: hallucinations, poor provenance, sycophantic acceptance of user premises, and a product design trade‑off that favours responsiveness over cautious refusal. These are not hypothetical problems; they produce wrong financial and legal advice with measurable consumer harm.
• Pragmatic path forward: vendors must continue improving provenance and safe defaults; regulators must set minimum safety and provenance standards for consumer AI used in regulated domains; and organisations must implement layered governance that balances productivity gains with legal and operational risk controls.

Finally, some claims in public reporting — notably exact usage numbers for particular vendor products or internal safety metrics — are reported unevenly across outlets and sometimes derive from vendor statements that are not independently audited. Those figures should be treated with caution until corroborated by primary disclosures or independent telemetry. When planning operational controls, rely on testable product behaviours and audited safety claims, not marketing milestones.

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Conclusion​

Which?’s consumer‑facing audit is a timely reminder that conversational fluency is not a proxy for fiduciary reliability. For Windows users, IT teams and enterprise risk managers, the imperative is clear: treat AI chatbots as powerful research assistants that require structured oversight — demand provenance, verify numbers against authoritative sources, lock down sensitive data, and insist on human sign‑off for decisions with legal, financial or safety consequences. The technology’s convenience is real and valuable, but without governance and product safeguards its popularity will continue to outpace its trustworthiness. The next product and regulatory cycles will need to close that gap; until they do, prudent users and defenders should assume the convenient answer is a first draft, not the last word.

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Source: MLex Meta AI, ChatGPT among AI chatbots giving risky advice, finds UK consumer group | MLex | Specialist news and analysis on legal risk and regulation