A recent headline in The Times warns of “fears of bias” on the grounds that ChatGPT supposedly relies on a single news outlet, often cited as The Guardian. While eye-catching, this claim misunderstands both how large language models work and what the underlying research actually shows.
ChatGPT does not “rely” on any one newspaper in the way a human reader might rely on a favourite daily. It does not read the news each morning, subscribe to particular outlets, or assign internal weightings such as “58 per cent Guardian, 12 per cent BBC”. There is no editorial desk inside the model. Instead, ChatGPT is trained on a vast mixture of licensed data, data created by human trainers, and publicly available text from many thousands of sources, including books, academic writing, news articles, and general reference material. The model does not have access to a list of its training sources, nor can it identify or favour specific publishers by design.
So where does the “Guardian dominance” claim come from? It originates from studies that analyse citations appearing in generated answers to a limited set of prompts. In other words, researchers ask the model questions, observe which publications are named in responses, and then infer bias from the frequency of those mentions. That is a very different thing from uncovering a built-in dependency.
Several factors explain why certain outlets appear more often in such studies. First, some publishers make their content more accessible for indexing and quotation, while others sit behind hard paywalls or restrict automated access. If a newspaper tightly limits how its material can be referenced or surfaced, it will naturally appear less often in AI outputs, regardless of its journalistic quality. This is an access issue, not an ideological one.
Second, when ChatGPT is asked to cite examples, it tends to reference outlets that are widely syndicated, heavily quoted elsewhere, and commonly used as secondary references across the web. The Guardian, like the BBC or Reuters, is frequently cited by other publications, blogs, and academic commentary. That secondary visibility increases the likelihood of it being named, even when the underlying information is widely shared.
Third, these studies typically involve small samples of questions. Changing the phrasing, topic, or timeframe can produce very different citation patterns. Extrapolating sweeping claims about “bias” from such narrow slices risks overstating the evidence.
Crucially, ChatGPT does not browse the news unless explicitly instructed to do so using live tools, and even then it does not default to a single outlet. When summarising current events, it aims to synthesise information from multiple reputable sources to provide balance and context.
The real conversation worth having is not about imagined loyalty to one newspaper, but about transparency, access, and how news organisations choose to engage with AI systems. Framing this as ideological bias oversimplifies a technical and structural issue.
In short, the claim that ChatGPT “relies on one news source” mistakes surface-level citation patterns for underlying dependence. It makes for a provocative headline, but it does not accurately describe how the system works, nor does it demonstrate the bias it implies.










