What's Next for B.I.A.S.

There is an uncomfortable truth at the heart of this project. B.I.A.S. selects for calm, non-political, globally-minded stories. That is itself a bias. We are biased toward certain subjects, certain tones, certain kinds of journalism. We said so in the name.

But the problem with bias in the news is not that it exists. It always has, and it always will. Every editorial decision — what to cover, how to frame it, whose voice to include, which detail leads the story — reflects a perspective. The journalist has one. The editor has one. The publication that employs them has one. So does the organisation that funds that publication. These perspectives shape what you read, often in ways that are invisible to you.

The problem is not bias. The problem is undisclosed bias — influence that presents itself as neutral, objective, or simply "the facts". That is where news becomes a weapon rather than a service.

Transparency over neutrality

Genuine neutrality is probably impossible. Claiming it is, at best, naive and, at worst, a form of manipulation — it implies the absence of a perspective that is actually just well-hidden. What is achievable, and far more useful, is transparency.

If you know that a story comes from a publication funded by a particular government, you can weigh it accordingly. If you know a source has a strong regional perspective, or a consistent political lean, or a commercial relationship with the industry it covers, that context changes how you read what they write. You do not have to distrust the story — you just have to read it with your eyes open.

This is not about telling readers what to think. It is about giving them what they need to think for themselves.

What this could look like

The next step for B.I.A.S. is to explore what bias transparency might look like in practice, alongside the editorial curation we already do. Not a single "bias score" — that would be its own form of oversimplification, and arguably its own bias — but a set of visible, honest signals.

Some possibilities we are thinking about:

• Funding and ownership signals — is this publication state-funded, privately owned, foundation-backed, or advertiser-supported? Each model creates different incentive structures and pressures.
• Regional perspective — where does this publication sit geographically and culturally? A story about trade policy reads differently from Brussels, Washington, and Nairobi.
• Framing indicators — does this story foreground individuals or systems? Solutions or problems? Conflict or cooperation? These are not quality judgements, but they are real differences in how stories are constructed.
• Editorial history — does this source consistently cover certain topics more than others? Certain regions? Certain perspectives on recurring issues?

None of these signals would tell you whether a story is good or bad, true or false. They would simply give you more of what you need to engage with it critically. The story stands on its own. The context helps you read it as an informed adult rather than a passive recipient.

Why this is hard

Classifying bias is itself a biased act. Any system that scores or labels publications will reflect the assumptions of the people who built it — including us. We are aware of this, and it is part of why we want to proceed carefully, openly, and with input from people who disagree with our instincts.

The goal is not a definitive taxonomy of who is biased and who is not. It is a set of factual, verifiable signals — funding model, ownership structure, geographic origin — that readers can interpret for themselves. The analysis stays with you. We just try to make the raw material visible.

We would like your input

This is an idea we are developing, not a feature we have built. Before we build anything, we want to understand how readers think about this. Some questions we are genuinely uncertain about:

• What signals would actually help you read more critically?
• What signals would you find patronising, misleading, or counterproductive?
• Are there existing frameworks or tools in this space that we should be aware of?
• Is there a version of this that could make things worse — that we should be careful to avoid?

If you have thoughts, we would genuinely like to hear them. You can reach us via the contact page, or follow the development of this idea on GitHub, where we discuss features and direction openly.

B.I.A.S. started as an antidote to doomscrolling. What it becomes next might be something more ambitious: a small contribution to the project of helping people read the news as active thinkers rather than passive consumers. That seems worth trying.