The policing scandal in Birmingham this week — in which a senior chief admitted that a Microsoft Copilot output helped produce a false intelligence claim that fed into a decision to ban visiting supporters — is not primarily a story about broken code. It is a story about broken leadership: about decision-makers who accepted machine-generated assertions as evidence, failed to demand provenance and verification, and then watched public legitimacy erode as a predictable cascade of errors became a political crisis.
In practical terms the route back to credibility is clear: invest in visible governance, require provenance, accept human-in-the-loop verification as non-negotiable for high-stakes decisions, and embed AI literacy at the top. These are organisational choices, not technical fantasies. They are the price of keeping powerful tools in the hands of responsible institutions.
If leaders grasp those realities and act decisively — not as an afterthought but as a core competency — then AI will remain the powerful amplifier of public service it promises to be. If they do not, the next Copilot hallucination will be less likely to be a single scandal and more likely to be a slow, systemic erosion of civic trust.
Source: lbc.co.uk https://www.lbc.co.uk/article/ai-public-trust-opinion-5HjdQwZ_2/]
Background
The episode that crystallised public anxiety
On 6 November, a safety advisory decision led to visiting supporters being prevented from attending a Europa League match in Birmingham. Subsequent inspectorate review found multiple inaccuracies in the intelligence used to justify that advice — including a reference to a historic fixture between Maccabi Tel Aviv and West Ham that never occurred. That fabricated item was later traced back to an output generated by Microsoft Copilot. Senior leadership initially denied that an AI assistant had been the source, then corrected the recorsaid she had “no confidence” in the chief constable and an accountability crisis followed. This is the practical problem the LBC opinion column highlighted: the technology did not make the mistake — the organisation did by treating the tool’s output as a verified fact. That misreading of responsibility is the crux: when leaders fail to understand the limits of the tools they approve, institutional trust is the real casualty.What the data says about public confidence in AI and institutions
Public scepticism about AI is not hypothetical. Major, independently conducted surveys show broad unease about the technology’s societal effects and limited confidence in institutions to govern it well. A wide-ranging Pew Research Centre study surveying 5,410 U.S. adults and more than 1,000 AI experts in 2024 found stark gaps between expert optimism and public anxiety: while a majority of surveyed experts expected net benefits from AI over the next two decades, the general public reported much higher levels of concern about harms and insufficient government oversight. The survey also found that both the public and many experts wanted stronger controls on AI. Global trust surveys tell the same story in different language. The Edelman Trust Barometer — the platform’s 2024–25 fieldwork encompassing some 33,000 respondents across dozens of countries — documented repeated declines in institutional trust and flagged confusion over what to trust as a dominant source of grievance. In many markets, people now rank tech platforms and AI companies as less trusted than they once were, and the trend has institutional consequences: when citizens believe decision-makers are misled (or misleading), legitimacy frays.Why leadership, not just product design, is the weak link
The human factors that convert a hallucination into a scandal
Modern generative assistants (Copilot, ChatGPT, Gemini, Claude and others) are large language models that generate fluent prose by predicting sequences of tokens. They are optimised for plausibility and helpfulness, not for guaranteed factual recall. As a result they sometimes “hallucinate”: inventing quotations, events or dates that sound real. When an organisation’s operating procedures treat these outputs as primary evidence without traceable sourcing, the technology’s probabilistic nature can be weaponised by institutional overconfidence. Human cognitive biases accelerate the problem. Decades of human‑factors research document automation bias — the tendency to trust automated outputs, especially under time pressure or when the outputs are presented authoritatively. As assistants produce ever more convincing prose, the psychological pressure to accept their answers increases, not decreases. Independent studies in human–machine interaction show that confidence cues and polished explanations raise acceptance even when the underlying evidence is weak. Finally, organisational friction matters. If leaders are distant from operational workflows, procurement accepts tools without operational policies, and front-line staff lack mandatory verification rules, then a single false output can migrate from a private chat to a public briefing — and from there into policy with dramatic consequences. Several internal post-incident analyses and practitioner threads that have since circulated inside public-sector communities catalogue exactly this chain and propose hard organisational fixes.The accountability paradox
The public expects two things simultaneously: that decision-makers use modern tools to become more efficient and that those same decision-makers be accountable when outcomes go wrong. When leaders publicly present facts derived from an assistant without a documented audit trail — who prompted the model, what prompts were used, which model version, and what corroborating primary-source checks were performed — it becomes difficult to trace responsibility. That opacity undermines the veryust preserve. The West Midlands episode made that tension politically visible.Evidence and verification: the missing infrastructure
What good governance would have looked like
Practical, documented governance is not academic spin: it is a set of concrete controls that would have prevented this cascade. Leading practitioner guidance, inspectorate recommendations and internal reform proposals converge on a small set of high‑impact measures:- Mandatort logs: every AI-assisted factual claim used in decision-making should be accompanied by source links, timestamps, prompts and identity of the user who requested/validated the output.
- Human-in-the-loop verification rules: treat AI outputs as hypotheses, not as evidence; require independent corroboration by a second analyst for high-impact or liberty‑constraining recommendations.
- Tool whitelisting and procurement standards: approve only enterprise-grade assistants configured with retrieval-augmented generation (RAG) over curated corpora and contractually available logs.
- Leadership AI literacy programmes: train sen basic failure modes of LLMs so they can read and question an intelligence chain before acting on it.
Technical mitigat buyers must demand
On the product side, several well-understood engineering mitigations reduce hallucination risk in operational workflows:- Retrieval-augmented generation (RAG): bind a model’s output to explicit, verifiable documents and force citation. This reduces free-form fabrication because every factual claim points to a retrieved record.
- Confidence and provenance signals: systems should expose confidence scores, source snippets, and explicit “I don’t know” refusals rather than producing plausible-sounding fabrications.
- Prompt and model version logging: retain immutable logs of prompts, model versions, and responses as part of evidence chains.
Why this matters beyond policing
Civic risk, not just reputational damage
The West Midlands example is an acute case, but the structural risk is systemic and crosses domains. When AI outputs become inputs to decisions about admission, parole, benefits, healthcare diagnosis or public-safety tactics, the consequences scale. Independent audits of AI assistants and broadcasters’ testing programmes have repeatedly shown widespread sourcing and factuality failures in news and information tasks — evidence that these tools are not yet ready to be treated as primary evidence across civic workflows. That degraded information integrity risks amplifying misinformation and undermining civic trust in institutions nationwide.The empirical picture on trust and regulation
Two reliable empirical threads are worth highlighting.- Surveys show the public wants more control and oversight, not less. Pew’s 2024 surveys found majorities across demographic groups worrying that regulation will be too lax and calling for stronger governance.
- Global trust audits (Edelman and public-broadcaster studies) show that while familiarity with AI can increase acceptance for some users, a large share of audiences still distrust the information ecosystem; when institutions fail to explain controls, trust declines. These trends create a political imperative for leaders to show — visibly and credibly — how they govern AI.
Hard choices for leaders: from governance to culture
What effective leadership looks like in the AI era
The central leadership requirement is simple in concept and hard in practice: translate technologistitutional competence. That requires three overlapping shifts:- Operationalise responsibility: if your organisation uses an AI tool for research, your policy must define who is acaim that reaches a public or operational decision. Leaders must be able to explain where an assertion originated
- Invest in AI literacy at the top: executive and board-level education must cover failure modes, audit requirements and procurement realities. Leaders who cannot explain governance choices should not certify high-stakes outputs.
- Change performance metrics: reward teams for verification and provenance work, not just for speed or the volume of actions produced by AI. Thentives away from convenience-driven error.
Tactical checklist for immediate remediation (step-by-step)
- Publish an organisation-wide AI usage policyesearch from evidence* and lists approved tools.
- Implement mandatory two-person verification for any AI-sourced factual claim used in operational briefings.
- Configure enterprise Copilot/assistant deployments to require RAG over curated corpora for intelligence tasks and enable prompt logging byicrosoft.
- Create a public-facing transparency report that explains where AI is used, what checks exist, and the steps taken when errors are found. Visible remediation builds trust faster than private fixes.
- Commission independent audits of AI outputs used in high-stakes public workflows and publish redacted versions of the findings and corrective actions.
The strengths and the risks: a balanced assessment
Not everything about AI adoption is negative
- Productivity gains are real. Enterprise pilots of Copilot-style assistants often report measurable time savings and improvements on structured tasks; in accounting, legal drafting and meeting summarisation these tools can amplify human labour when properly governed.
- Innovation momentum matters. Sidelining AI entirely would forego efficiency gains and competitive advantage for public services and private firms alike. The challenge is to balance speed with controls.
But the downside is asymmetric and political
Errors that might be forgiven in a back-office content draft become existential when they affect civil liberties, safety decisions, or minority communities’ rights. The public reaction to leadership failures is rarely proportional to technical subtlety; it is political and reputational. That asymmetry means leaders must be especially conservative in how they certify AI-assisted outputs for public action. The West Midlands case shows the political voltage of such missteps.Conclusion: restoring credibility requires competence and humility
The lesson is not that leaders should avoid AI; it is that they must stop pretending AI is a neutral, self‑evident source of truth. Public trust is a fragile asset built on clarity about who did what, when and why. When leaders cannot explain the provenance of a claim because they never demanded documentation in the first place, trust collapses quickly — and repairing it costs far more than the prudence required to prevent the failure.In practical terms the route back to credibility is clear: invest in visible governance, require provenance, accept human-in-the-loop verification as non-negotiable for high-stakes decisions, and embed AI literacy at the top. These are organisational choices, not technical fantasies. They are the price of keeping powerful tools in the hands of responsible institutions.
If leaders grasp those realities and act decisively — not as an afterthought but as a core competency — then AI will remain the powerful amplifier of public service it promises to be. If they do not, the next Copilot hallucination will be less likely to be a single scandal and more likely to be a slow, systemic erosion of civic trust.
Source: lbc.co.uk https://www.lbc.co.uk/article/ai-public-trust-opinion-5HjdQwZ_2/]
