Evidence before excitement
We separate company claims from independently established facts, link to primary material and explain uncertainty.
Plain language
We make complex ideas approachable without pretending they are simpler than they are.
People remain accountable
Automation can help collect, compare and check information. A human editor remains responsible for every published story.
Visible correction
When we make a meaningful error, we correct it promptly and add a clear note describing the change.
Sourcing and fairness
We prefer original documents, official datasets, research papers and direct, on-record interviews. We identify advocacy reports as advocacy reports and company claims as company claims. When a person or organisation faces serious criticism, we seek a meaningful opportunity for response.
How we use AI
AI tools support discovery, comparison, drafting and image creation. A story can enter the publishing workflow only after it passes our source, clarity, uncertainty, usefulness, accuracy and image-rights checks. Material claims are checked against the cited sources, and company or government claims remain attributed. We do not invent quotes, sources, reactions or reporting. Generated artwork is labelled as an editorial illustration. A human publisher can review, revise or correct any article after publication.
Corrections
Factual errors are corrected in the article. Material corrections receive a dated editor’s note explaining what changed. Typos or style changes that do not alter meaning may be fixed silently. Send correction requests to contact@modelcurrent.com with the article URL and supporting evidence.
Commercial independence
Advertising and sponsorship do not buy favourable coverage or advance access to editorial conclusions. Sponsored material is clearly labelled. Affiliate links, if introduced, will be disclosed near the link.