The Editorial Test We Apply Before Running Any AI Statistic
A simple internal checklist for whether a cited AI statistic is solid enough to run without further qualification.
A simple internal checklist for whether a cited AI statistic is solid enough to run without further qualification.
Reviewing our own opinion coverage over time surfaced a pattern we hadn't noticed while publishing it piece by piece.
Confirmation bias is easiest to catch in arguments you disagree with. It's most dangerous in the ones you don't question…
Running a well-argued piece we disagree with is a deliberate editorial choice, not an oversight. Here's the reasoning behind it.
A bylined executive piece co-written with the person who actually ran the initiative tends to be more specific and more…
Reviewing rejected guest pitches as a pattern, not individually, revealed a recurring gap worth addressing directly.
Most opinion writing implicitly assumes an already-sympathetic reader. Naming the skeptical reader you're trying to reach sharpens the argument.
Some AI questions are genuinely uncertain. Others just haven't been measured carefully yet. Analysis often treats these the same way.
Asking an expert how they'd bet money on their own prediction produces more honest calibration than asking for the prediction…
AI facts change fast enough that publications need a clearer line between routine updates and genuine corrections of what was…