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Everything we’ve published under Trust so far.
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Trust appears in trust, safety, and evaluation, a useful buying-intent lane for relevant AI products and services.
Ladybird is no longer accepting public pull requests because AI-assisted code has changed what a patch proves. The useful lesson is not anti-AI. It is that responsibility, review capacity, and security boundaries now matter more than contribution volume.
OpenAI’s Privacy Filter sends a clear cultural message: useful AI needs boundaries that are visible enough for users to actually trust it with their real work.
OpenAI’s new agent observability tools sound like developer jargon, but they represent the difference between useful delegation and finding out your bot rearranged the CRM while you were asleep.
Partnership on AI’s take on assurance reminds us that public trust isn’t built on launch demos. It’s built on standards, monitoring, and the boring machinery that proves an AI isn't hallucinating its way through your data.
The Reuters Institute's Digital News Report highlights a familiar media crisis and a new behavior: people are asking chatbots for the news. The interface is changing faster than the trust rituals can adapt.
The AP treats generative AI as unvetted source material and bans it from creating publishable content. It’s an unusually clean defense of human accountability in an era of automated confidence.
The U.S. Copyright Office’s AI reports provide a public record for the cultural argument artists are making: what happens when human labor becomes the training substrate for its own replacement?