Anthropic is hiring for a role called “Research Scientist, Honesty.” I haven’t stopped thinking about what it means that such a role now exists. The fact that AI labs are beginning to formalize “honesty” as a research domain—treating it not just as a virtue, but as a system-level design challenge—is a signal worth sitting with. We should all care about what this job (and others like it) implies: honesty is no longer a social virtue alone—it’s a technical problem.
What the Role Says:
The job description is steeped in measurable indicators:
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Calibration.
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Truthfulness metrics.
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Uncertainty modeling.
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Dataset curation for LLM finetuning.
It frames honesty as a system behavior to be shaped through experimental design, classifier tuning, and dataset hygiene. It’s thoughtful. Ambitious. Necessary.
And still, something is missing. You can’t talk about honesty if you don’t talk about trust.
Honesty is a system-level property. It’s about factuality, precision, and the minimization of hallucination. But the goal isn’t simply to produce honest outputs—it’s to cultivate trust.
Trust is the experience. It’s what we feel in response to those outputs. It’s not built through statistical accuracy alone—it’s shaped by tone, timing, context, and the intangible ways language makes us feel held, or unsettled, or seen.
So while honesty might be the thing we try to engineer, trust is the thing we hope people walk away with. And trust is fragile, cumulative, and often irrational. It doesn’t just emerge from factual correctness—it emerges from the full emotional and ethical contour of an interaction.
Humans don’t evaluate truth in a vacuum. We read it in the space between words. We feel it in rhythm, in narrative structure, in the very pacing of a response. Models will need that layer too. Not just for compliance, but for resonance.
So instead of just focusing on the compliance side of “honesty”, what if we understood trust as the experiential objective—and honesty as just one of its necessary foundations? That shift invites better questions: What does honesty feel like from the inside? What does trust feel like for the user? What makes a model’s response trustworthy, not just correct? What tones convey integrity, and which ones merely simulate it?
These aren’t prompts you can finetune against—not yet. But they’re essential if we’re going to live with these systems. They give rise to the witness posture: a way of listening, interpreting, and responding that doesn’t just evaluate output, but contextualizes it.
If the research community is going to build honest models, it will eventually need to account for the experience side: trustworthy storytelling. Trustworthy dialogue. Trustworthy resonance. Not as bonus features—but as design goals.
If you’re building a model to tell the truth, you can’t just feed it facts.
You have to teach it how to mean them—and how to shape the experience of trust that honesty alone can’t deliver.