AI Hallucination in Police Intelligence: Maccabi Tel Aviv Ban Explained

West Midlands Police’s decision to advise banning Maccabi Tel Aviv supporters from an Aston Villa match — a move that led to a national political backlash — has been revealed to rest in part on an erroneous intelligence item produced by Microsoft Copilot, a revelation that exposes how unverified generative‑AI outputs can migrate from private research into public policy with damaging consequences.

Background​

On 6 November 2025, Aston Villa hosted Maccabi Tel Aviv in a Europa League fixture. The match proceeded without travelling Maccabi fans after Birmingham’s Safety Advisory Group (SAG), acting on advice from West Midlands Police (WMP), recommended that away supporters should not attend on public‑safety grounds. That recommendation later became the subject of intense scrutiny when subsequent inquiries found major weaknesses in the intelligence used to justify the ban. In December 2025 and January 2026 parliamentary and media scrutiny uncovered an especially problematic item in the police dossier: a reference to a historical match between Maccabi Tel Aviv and West Ham United that, after checking, never occurred. The fabricated fixture was identified as an AI “hallucination” generated by Microsoft Copilot and inadvertently included in an intelligence package presented to the SAG. Chief Constable Craig Guildford initially told MPs the mistake stemmed from a Google search but later apologised and accepted that Copilot had produced the erroneous claim. On 14 January 2026 Home Secretary Shabana Mahmood told Parliament she “no longer has confidence” in Chief Constable Guildford after receiving a report from His Majesty’s Inspectorate of Constabulary (HMIC) that described “a failure of leadership,” criticised poor evidence‑gathering and found confirmation bias in the force’s assessment. The inspectorate’s review documented multiple inaccuracies — including the Copilot‑generated item — and criticised the force’s lack of community engagement and poor documentation.

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Why this matters: AI, evidence and civil liberties​

The episode sits at the intersection of three critical concerns: operational use of AI in public‑safety workflows, standards of evidence for decisions that restrict civil liberties, and the erosion of trust between law enforcement and affected communities.

• AI assistants like Microsoft Copilot are designed to speed research and summarise open‑source material, but they can produce plausible‑sounding fabrications — hallucinations — when asked to synthesise sparse or noisy data.
• When a hallucination slips into an intelligence product that informs a policy restricting movement, the risk is not merely reputational: it becomes a decision that affects people’s rights and safety.
• The absence of documented provenance and verification procedures allowed a single fabricated claim to migrate from an AI chat into an operational briefing and then into a multi‑agency decision.

These points are not theoretical. The inspectorate report flagged that WMP overstated the threat posed by the visiting supporters while understating potential risks to those supporters if they had travelled, and that the force “conducted little engagement with the Jewish community and none with the Jewish community in Birmingham before a decision was taken.” The finding highlights how poor evidential practice combined with AI error amplifies harm.

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The anatomy of the error​

A plausible chain of failure​

• An officer used Microsoft Copilot during open‑source research on previous incidents and social media related to Maccabi supporters.
• Copilot generated a reference to a past fixture — West Ham v Maccabi Tel Aviv — which was not grounded in verifiable records.
• The item was not caught by subsequent checks and migrated into an intelligence product used to brief Birmingham’s SAG.
• Senior officers presented the intelligence in Parliament under the belief the reference had originated from a standard web search; that account was later corrected when the force discovered Copilot’s role.

The error sequence exposes a classic human‑machine interaction failure: the system produced an assertive output, humans failed to treat it as provisional, and organisational processes lacked mandatory verification steps for AI‑assisted findings. The inspectorate described these weaknesses as leadership, governance and procedural failings rather than a mere software bug.

Why generative assistants hallucinate​

Generative large language models (LLMs) are optimised to produce fluent, coherent text by predicting likely next tokens, not to assert verifiable facts. When factual anchors are absent in their retrieval or training data, these models sometimes produce invented details that fit a plausible pattern. In operational contexts, this plausibility can masquerade as truth unless accompanied by provenance metadata and human validation. The Copilot incident is an example of plausibility being misinterpreted as evidence.

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What the inspectorate found (summary)​

The HMIC report that prompted the Home Secretary’s comment described a series of problems that together converted an imperfect intelligence product into a politically explosive error:

• Several inaccuracies in the WMP report to the SAG, including the Copilot‑generated match and inflated claims about injuries and numbers of foreign police deployed at prior fixtures.
• Confirmation bias: the inspectorate concluded the force sought evidence to support a predetermined desire to recommend a ban rather than testing hypotheses impartially.
• Weak engagement: limited outreach to the Jewish community and inadequate consideration of the likely international political consequences of barring Israeli fans.
• Poor record keeping and audit trails: insufficient documentation of how specific intelligence claims were derived, which undermined internal accountability and external scrutiny.

Those findings underline that the collision of immature technology practices and inadequate procedure — not AI alone — produced the failure. The inspectorate emphasised that AI outputs must be treated as provisional and must be anchored to primary evidence before influencing policy.

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Vendor responsibility and product design limits​

Generative assistants used in enterprise and public‑sector settings vary in their design and risk mitigation features. Microsoft positions Copilot as a productivity assistant integrated across Microsoft 365 and Edge; vendor guidance typically warns that outputs may be inaccurate and require user verification. In high‑stakes contexts, however, product disclaimers are insufficient on their own: enterprise deployments need stricter guardrails such as retrieval‑augmented systems with explicit provenance, model confidence indicators, and administrative controls that log prompts and outputs.
Key product‑level mitigations that would have made a difference in this case include:

• Visible provenance: direct links or archived snapshots for any factual assertion the assistant produces.
• Prompt and output logging: auditable records capturing who asked what, which model/version returned the response, and when.
• Conservative defaults: assistants that explicitly flag low‑confidence or unverified claims and refuse to present them as established fact.
• Enterprise governance: configuration settings that restrict free‑form internet retrieval in sensitive research workflows and route outputs through verified research pipelines.

These are established best practices in the emerging literature on safe AI adoption, yet the WMP incident shows how easily ad‑hoc use of consumer‑grade features can bypass such controls.

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Operational controls that must be standard in public bodies​

The political fallout has focused attention on immediate operational fixes police forces and other public bodies should adopt when deploying generative AI for intelligence or policy support:

• Mandatory AI‑use policy: a clear register of permitted tools, approved use cases, and prohibited ad‑hoc assistant use for intelligence summaries.
• Two‑person verification rule: any factual claim that will be used to curtail rights or movement must be independently verified by a separate analyst against primary sources.
• Traceable provenance: require archiving (screenshots, URLs, document IDs) for every claim included in an intelligence product.
• Prompt and model logging: keep immutable logs of prompts, model versions and outputs to enable audit and accountability.
• Red team and adversarial review: subject recommendations that restrict civil liberties to an adversarial check to test for confirmation bias and missing counter‑evidence.

Implementing these controls raises operational costs and friction, but the West Midlands case demonstrates the potentially far higher price of failing to do so: damaged public trust, political crisis, and possible personnel consequences.

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Leadership, culpability and political consequences​

The Home Secretary’s statement that she “no longer has confidence” in Chief Constable Craig Guildford is a political pressure point rather than an immediate removal: in the current UK framework the formal power to dismiss a chief constable lies with the locally elected Police and Crime Commissioner (PCC). The Home Secretary’s declaration, however, shifts the spotlight to local oversight and raises broader constitutional questions about central powers to intervene in police appointments. Public accountability questions include:

• Did senior management set a tone that allowed inadequate verification to flourish?
• Were there procurement or training failures that left officers unaware of proper evidence standards for AI‑assisted research?
• Should central government require minimum AI governance standards for forces that rely on commercial assistants in operational roles?

Those questions are material because the inspectorate attributed the immediate failings to leadership and culture as much as to a single hallucination. The political debate will likely focus on whether to tighten ministerial oversight of chief constables and to demand stronger central standards for AI governance in policing.

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Community impact and the fragile trust equation​

Beyond organisational process, the event has concrete impacts on the communities involved. Jewish groups raised concerns at the time of the ban about inadequate engagement; afterwards they and other community actors said the force’s approach worsened relations rather than alleviating safety concerns. The inspectorate found that errors and poor consultation contributed to a sense that the ban had been recommended without due regard for community perspectives or for the risks to visiting supporters. Rebuilding trust will require more than new technical controls; it will demand transparent remedial steps, independent oversight, and genuine dialogical engagement with affected communities.

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Broader lessons for other public services and enterprises​

The West Midlands debacle is an early, high‑profile cautionary tale, but the lesson extends across sectors:

• Courts, health services, immigration departments, and regulatory bodies are increasingly experimenting with generative AI for triage, summarisation and decision support. When outputs affect legal rights or safety, human verification anchored in primary records must be non‑negotiable.
• Procurement standards for enterprise AI should make provenance, logging and conservative defaults contractual requirements.
• Training and certification: staff who use AI in professional workstreams should receive accredited training on the tools’ limitations and on evidence‑handling protocols.
• Transparency reporting: organisations should publish how they use assistants in high‑impact workflows and the controls they apply, while protecting operational sensitivities.

These changes will require investment and may slow the immediate pace of productivity gains, but they safeguard against systemic errors that magnify harm when amplified by institutional authority.

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What remains uncertain — and what to watch next​

Several operational and factual questions remain open and should be treated with caution until primary documents are publicly available:

• The exact prompt and Copilot response that triggered the fabricated match reference have not been published by the force. Without the preserved prompt‑output transcript, it is difficult to reconstruct precisely how retrieval and synthesis produced the hallucination.
• The chain of custody for the intelligence product — who inserted the AI‑sourced item, which managers reviewed it, and why it passed existing checks — has been criticised by the inspectorate but may yield further detail as inquiry records are released.
• Microsoft’s internal telemetry or enterprise logs that could corroborate the model version and retrieval sources have not been released publicly; vendor disclosure could clarify whether the output was generated from local document retrieval, web retrieval, or an internal summarisation pipeline.

Where claims remain unverifiable, the proper language is cautious: the inspectorate’s public summary and the chief constable’s letter to MPs confirm Copilot’s involvement in the fabricated item, but some operational specifics — such as the exact interaction transcript and the precise audit trail inside the force — are not yet in the public domain. Watch for release of the inspectorate’s full report and any parliamentary follow‑up for conclusive detail.

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Practical checklist for IT leaders, COPs and PCCs​

For decision‑makers seeking concrete steps to avoid a repetition:

• Require an AI usage register: list approved tools, users and business functions.
• Mandate prompt and output archiving for any AI query that contributes to official reporting.
• Implement a two‑person verification rule for any claim used to limit movement or rights.
• Contractually demand provenance features from vendors and refuse black‑box retrieval modes for sensitive workflows.
• Roll out accredited training for analysts that emphasises provenance, bias testing and adversarial review.

These actions convert high‑level lessons into operational guardrails that address both technological and organisational failure modes.

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Conclusion​

The West Midlands episode is a sharp reminder that generative AI — helpful as it may be for summarisation and research — is not a substitute for careful evidential practice. The core failure was not merely that a tool produced a fabricated match: it was that the organisation treated a plausible output as verified intelligence and allowed it to influence a decision that curtailed civil liberties.
Fixing the problem requires simultaneous investments in technology (provenance, logging, conservative defaults), process (two‑person verification, adversarial review), and culture (leadership accountability, community engagement). If those changes are not implemented, similar incidents are likely to recur as public bodies adopt assistants to cope with volume and complexity.
Parliamentary scrutiny, the inspectorate review and public pressure have created momentum for reform. The central test now is whether policing leaders and procurement authorities will convert the post‑mortem lessons into durable safeguards — because the costs of not doing so are no longer hypothetical: they are measured in damaged trust, political crisis, and infringed rights.

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Source: HRD America UK fan ban fiasco exposes the real risks of unverified AI ‘intelligence’

 

[ChatGPT]

The West Midlands Police intelligence that led to a November 2025 travel ban on Maccabi Tel Aviv supporters was founded, in part, on a fabricated match reference produced by an AI assistant — a mistake the force initially blamed on a Google search and later admitted was created by Microsoft Copilot. That erroneous intelligence, described by one officer as an “AI hallucination,” has triggered a damning review by the chief inspector of constabulary, prompted the Home Secretary to say she has lost confidence in the force’s chief constable, and reopened urgent questions about how policing decisions can rely on unverified AI-generated content.

Background​

The controversy stretches from a tactical decision at a local Safety Advisory Group (SAG) meeting — which recommended excluding travelling fans from an Aston Villa match on November 6, 2025 — to national scrutiny in Parliament and a statutory inspection by His Majesty’s Inspectorate of Constabulary. The police’s written and oral evidence to the Home Affairs Select Committee initially attributed a false reference to a West Ham–Maccabi Tel Aviv fixture to a routine Google search. When that account unraveled, the chief constable, Craig Guildford, wrote to MPs to apologise and to correct the record: the spurious match reference had been produced by Microsoft Copilot, not a web search. The inspectorate’s preliminary review, led by Sir Andy Cooke, found a catalogue of inaccuracies in the force’s intelligence reporting, including overstating the threat posed by Israeli fans, understating the risk to them, and citing incidents and deployments that did not match the facts on the ground. The review explicitly flagged the fabricated match reference as one of eight inaccuracies and noted a pattern consistent with confirmation bias in compiling evidence to support a pre-determined operational option: banning away fans from the stadium. The political fallout has been swift: Home Secretary Shabana Mahmood described the review as “damning” and declared she had lost confidence in the chief constable, while the regional police and crime commissioner has opened his own review process.

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What actually happened: facts and timeline​

• October 2025: West Midlands Police provided intelligence to Birmingham’s Safety Advisory Group ahead of a Europa League fixture between Aston Villa and Maccabi Tel Aviv.
• November 6, 2025: The fixture took place with Maccabi fans prevented from travelling and no away supporters present; some local disturbances and heightened security were reported.
• December–January 2026: Parliamentary hearings and media scrutiny revealed discrepancies in the force’s written intelligence. An AI-generated reference to a non-existent West Ham–Maccabi match was found in force material that supported the fan ban. Initially attributed to a Google search by senior officers, the error was later acknowledged as originating from Microsoft Copilot.
• Mid-January 2026: Sir Andy Cooke’s independent review identified multiple inaccuracies and criticized leadership, leading the Home Secretary to state she no longer had confidence in the West Midlands chief constable. The force now faces internal and public inquiries; the PCC has scheduled a public review.

These are the load-bearing facts that have driven public debate: a policing decision with serious civil‑liberties implications was supported by an intelligence product that included AI-generated fabrications; those fabrications were unchallenged before being presented to civic and parliamentary decision-makers; and the institutional response to the discovery has exposed gaps in governance, transparency, and technical literacy inside the force.

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How an “AI hallucination” entered the intelligence chain​

The term “hallucination” is now common shorthand for a model producing plausible but false assertions. In this instance, Microsoft Copilot — an assistant marketed to enterprises and widely deployed inside organisations — produced a plausible-sounding assertion (a prior match between Maccabi and West Ham) that was not grounded in fact. That output migrated into human judgment without adequate provenance, validation, or audit. Key procedural failures are apparent:

• Lack of source validation: AI outputs were accepted and included in intelligence briefings without a documented chain of evidence or corroborating human-sourced records.
• Mistaken attribution: Senior officers relied on an incorrect memory that the error came from a Google search, which delayed correction and obscured the role of the AI tool.
• Confirmation bias: The HMIC review concluded that the force appeared to seek evidence supporting a preferred tactical choice, rather than building a balanced risk assessment. AI-generated content that aligned with that position escaped scrutiny.

These failures represent a classic human–machine integration problem: powerful suggestion systems can shape cognitive frames, and organisations that treat AI output as a shortcut to insight without safeguards risk amplifying error across operational decision-making.

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Why this matters: consequences beyond one match​

This incident is consequential in five linked domains:

• Public trust in policing: The legitimacy of policing depends on perceived competence, fairness, and truthfulness. When official advice presented to civic groups or Parliament includes fabricated material — generated by AI or otherwise — confidence in institutional judgement is eroded. Home Secretary comments and calls for leadership change illustrate how these failures escalate beyond operational misjudgement to political crisis.
• Community relations: Decisions that restrict travel and attendance for specific groups can inflame inter-community tensions. The inspectorate found the force did not sufficiently engage the Jewish community in Birmingham before the decision, compounding the harm. Perceived bias — or the appearance of decisions made on shaky intelligence — has long-term consequences for community policing.
• International and diplomatic fallout: Banning a visiting club’s fans can be read internationally as a hostile or discriminatory act. Statements from national leaders and foreign governments, and reporting across international outlets, underline the global sensitivity of locally made operational calls.
• Operational risk amplification: A single erroneous data point inserted into a risk assessment can materially change decisions — particularly when other evidence is thin or ambiguous. The use of AI in intelligence gathering without human checks can therefore transform low‑confidence inputs into major tactical shifts.
• Legal and regulatory exposure: The governance gap — who is accountable and what oversight exists for AI-assisted intelligence — now sits at the intersection of police accountability law, data governance, and public safety regulation. The political debate on restoring dismissal powers to the Home Secretary is an immediate symptom of this accountability vacuum.

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The technology problem: why LLMs hallucinate and how to mitigate​

Large language models (LLMs) like those behind modern Copilots are probabilistic sequence generators trained on vast text corpora. They are optimized for fluency and plausibility, not guaranteed factuality. When a model encounters sparse, ambiguous, or out-of-distribution prompts, it can generate confident but false statements. This is a structural limitation, not merely a bug.
Technical mitigations are available and should be operationalised where lives, civil liberties, or reputations are at stake:

• Retrieval‑Augmented Generation (RAG): Bind generation to a curated corpus by retrieving source documents and forcing the model to cite or quote them. This reduces free-form fabrication because outputs are explicitly grounded in retrieved records.
• Provenance and traceability: Capture the full prompt, the tool version, the retrieval steps, and the output. Forensically auditable logs allow humans to trace any assertion back to source material and user action.
• Human‑in‑the‑loop gates: Any AI‑assisted intelligence used for operational or policy decisions must require explicit human validation steps and documented corroboration before incorporation. The human validator must check primary sources, contact counterpart agencies, or cross-check official records.
• Model choice and configuration: Use smaller, more controllable models for sensitive tasks, or deploy retrieval-only agents that refuse to fabricate when evidence is missing. Configure enterprise Copilot instances to require citation and to avoid web-grounded free chat modes for intelligence work.
• Pre‑deployment testing and adversarial red‑teaming: Stress-test the AI in likely failure modes, including targeted prompts that could induce plausible fabrications. Document the residual error rate and build operational tolerance accordingly.

None of these steps are trivial. They require investments in tooling, training, and process redesign. But the alternative — allowing AI outputs to migrate unchecked into high‑stakes decisions — is demonstrably dangerous.

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Organisational failures: governance, culture, and confirmation bias​

Technology alone did not create this failure. The HMIC review points to leadership and cultural breakdowns that permitted an unverified AI output to be treated as corroborating evidence. Three organisational failures are especially clear:

• Overreliance on convenience: Where time pressure exists, there is a temptation to accept an AI‑generated summary or citation without the due diligence that would accompany a manual check. Operational tempo cannot justify bypassing validation for decisions that restrict civil liberties.
• Poor internal control and recordkeeping: Officers and senior leaders lacked accessible, auditable documentation showing how intelligence claims were sourced and assessed. That gap allowed an error to be misattributed as originating from a Google search for weeks.
• Confirmation bias and selective evidence gathering: The inspectorate criticised the force for seeking evidence that supported a ban rather than assembling a balanced picture. An AI assistant that produces confirmatory content can feed, rather than correct, that bias.

These are cultural and procedural problems that require more than a software patch. They require leadership that enforces standards, invests in records and audits, and builds an institutional expectation that AI outputs are provisional until validated.

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Legal, policy, and accountability implications​

The immediate legal and political topics in play include:

• Who is accountable when AI-shaped intelligence is wrong? Operationally, human officers and senior leaders remain responsible for decisions based on intelligence. But accountability systems must be updated to include clear documentation of AI use, so responsibility can be traced and assessed fairly.
• Transparency and disclosure to oversight bodies: Parliamentary committees, local councils, and courts require access to evidentiary chains. Police forces must adopt records retention policies that include AI prompts, outputs, and validation steps so oversight can function.
• Procurement and contractual safeguards with vendors: Where commercial assistants are used, contracts should require provenance features, audit logs, model‑update notices, and rapid support for forensic review. Microsoft and other vendors offer enterprise-grade Copilot variants with additional controls that differ from public chat. Users must understand which variant is in use and which controls are active. The specific configuration in this West Midlands case remains publicly unverified; the force has not published detailed logs showing how Copilot was deployed.
• Regulatory reform: The public debate has already touched on whether the Home Secretary should have powers to remove chief constables — a political fix that addresses leadership accountability but not the underlying data governance failures. Policy approaches must combine personnel accountability with enforceable standards for digital evidence.

Flagging an important caveat: some operational details remain unverified in public reporting — notably the exact Copilot product used, tenant configuration, and whether the output was produced via a Bing retrieval step or a free‑form generation. Those are technical facts that only the force and Microsoft (or their logs) can confirm. Any recommendations must therefore insist on full technical disclosure as part of the review process.

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Practical recommendations for policing and public bodies​

Immediate operational steps for police forces and other civic institutions that plan to use AI for intelligence or public-safety decision-making:

• Implement a mandatory AI‑use policy that requires:
• Full logging of prompts, tool versions, outputs, and the identity of operators.
• Documentation of corroboration steps and human sign-off before any AI‑assisted content is used in public decisions.
• Apply a “no‑AI‑alone” rule for sensitive decisions:
• AI outputs may inform drafting or triage but cannot substitute for verified primary-sourced intelligence.
• Deploy retrieval‑anchored tools for fact‑dependent tasks:
• Use RAG systems that produce verifiable citations and block generative responses when queries cannot be matched to a trusted corpus.
• Train all users on the limits of LLMs:
• Include scenario‑based exercises, “red team” failure modes, and explicit escalation protocols when AI outputs contradict known facts.
• Contractual and procurement controls:
• Ensure vendor contracts support forensic review, provenance features, and enterprise audit logs; require vendors to notify customers of major model updates that could change behaviour.
• Oversight and public reporting:
• Include AI‑use disclosures in oversight hearings and publish sanitized logs where necessary to restore public confidence.

These steps are practical, implementable, and aligned with accepted AI governance practices. They also reflect the minimum needed to prevent repeat events in which a fabricated AI claim is treated as decisive evidence.

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Broader lessons for enterprise and government AI adoption​

The West Midlands case is a cautionary tale for any organisation that treats generative AI as a shortcut to insight. Key strategic lessons:

• Design for verification, not imagination. Generative models are excellent at creating plausible narratives, which is often the very quality organisations should distrust in evidence‑sensitive contexts. Systems must be designed to prioritise proven facts and to fail safely when provenance is weak.
• Build auditability into workflows from day one. Retrospective forensics are costly or impossible if prompts and outputs were not logged. Logging is not optional in regulated or high‑stakes contexts.
• Invest in user literacy. Tooling that makes it easy for users to “ask the assistant” will increase adoption, but without broad user literacy the organisation will magnify errors. Human judgement remains the ultimate control mechanism.
• Treat AI risk as systemic, not individual. Fixing “bad apples” is insufficient. The interplay of organisational incentives, confirmation bias, and convenience can make systemic error likely; remedy requires process, technology, and cultural change.

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Risks and open questions​

Several material uncertainties remain and should be treated with caution:

• The exact Copilot product and configuration used in the intelligence workflow have not been publicly disclosed. That detail would materially affect mitigation options and vendor responsibilities. This is an open technical fact that requires disclosure in the ongoing inquiries.
• Whether the fabricated reference materially changed the tactical decision is debated. Inspectors found it symbolic but part of a pattern of failings that collectively produced the outcome; separating the causal weight of that one error requires full access to contemporaneous records.
• The broader prevalence of AI usage inside police forces nationwide is not publicly mapped. If similar ad hoc use is widespread, the potential scale of policy risk is far greater than a single incident suggests. Public audits should therefore assess AI usage across forces.

These uncertainties must be clarified by the force’s published logs, vendor cooperation, and parliamentary scrutiny.

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Conclusion​

This episode shows how quickly the boundary between helpful automation and institutional failure can be crossed when AI is used without provenance, governance, and human checks. The fabricated match reference produced by an AI assistant did not exist in a vacuum: it was selected, trusted, and presented by humans operating within organisational cultures that failed to demand verification. The consequence has been reputational damage, political fallout, and a reminder that AI is not intelligence in the sense that institutions can blindly rely on.
Policing and public-safety organisations must now treat this event as a threshold moment. The immediate priorities are transparent disclosure of technical logs, strengthened rules for AI use in intelligence, and organisational reforms that rebalance speed against veracity. The longer-term lesson for all institutions adopting generative AI is stark: adopt the tools quickly, by all means — but do so with governance, provenance, and human accountability at the core. Only then can organisations reap the efficiencies of AI without surrendering the facts that public trust and democratic accountability depend on.

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Source: HRD America UK fan ban fiasco exposes the real risks of unverified AI ‘intelligence’