Topic

Accuracy, Safety and Trust

Why AI states wrong things confidently, how to catch it before you publish, and the privacy and disclosure questions that come with using these tools at work.

The dangerous failures are not the obvious ones. A model that clearly misunderstands you is easy to dismiss; a model that returns a fluent, well-structured, entirely invented citation is not. Everything in this hub follows from one fact — these systems produce likely text rather than retrieved fact, and have no idea which of their answers are the shaky ones. That makes verification a habit rather than an optional extra, and it makes a small number of practices worth learning properly: what to check, how to ground answers in real sources, and what never to paste into a tool in the first place.

Sort by what a wrong answer costs

This single distinction prevents most of the damage people come to regret. Brainstorming, rewriting and summarising something you can see are low risk, because you would notice a mistake. Facts, figures, citations, quotations, names, dates, legal rules and anything you will repeat to someone else are high risk and get checked against a real source before use. Asking the model whether it is sure is not verification — it will apologise and produce a different answer with equal confidence.

Grounding is the structural fix

Where the problem is that a model does not know something specific to you — your documentation, your policies, your data — retrieval is the answer rather than fine-tuning: it is cheaper, updates instantly, and can cite its source. The quality of a grounded system is decided before the model is involved, in how documents are chunked and whether the retrieved passages actually contain the answer. When answers are wrong, look at what was retrieved before blaming the model.

A citation that exists is not a citation that supports the claim

This is the failure mode that survives a careless check. A model will attach a real, resolving source to a sentence that source does not support, and a status check on the URL confirms nothing about whether the claim is in it. Verify against primary sources, check the specific claim rather than the topic, and notice when several citations trace back to one original — fluent synthesis erases that distinction completely.

What not to paste

Personal data, client confidential material, employee records, patient information and anything under a regulatory framework belong only in tools your organisation has approved for that purpose — a question to settle before anything is typed rather than after. Where you can, strip identifiers before the data reaches a model: most requests work perfectly well with names and account numbers removed, and that sidesteps most of the question entirely.

Verify anything you will act on or repeat, ground answers in sources you can open, and decide what may be pasted where before the situation is urgent. These are not sophisticated practices — they are the difference between AI as a tool that saves you time and AI as a way to publish something confidently wrong faster than you could before.

Articles

Catching AI Errors: Multi-Model Checking That Actually Works

Why models state wrong things confidently, how running the same question past several models surfaces errors, and a verification routine you can apply in minutes.

Screening AI Content Before You Publish It

The checks worth running on AI-assisted content — accuracy, originality, brand and legal — and the review step that decides whether the pipeline is an asset or a liability.

AI Detectors: How They Work and Why They Get It Wrong

What AI content detectors actually measure, why their accuracy claims do not survive contact with research, who gets hurt by false positives, and what to do if you are accused.