#trust

Concepts exploring "trust"

Calibrated Autonomy

Autonomy isn't binary — it's calibrated to consequence magnitude. The same tiered governance pattern recurs across institutions, AI alignment, and agent orchestration.

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Brand as Proxy for Trust

When technical verification of AI properties is impossible, institutional reputation becomes the trust anchor — with all the fragility that implies

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Constitutional AI vs RLHF

Different alignment approaches produce different failure modes — RLHF optimizes for human approval, Constitutional AI optimizes for principle-adherence, with different implications for honesty and reliability

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Drift

Gradual changes in model behavior over time, even without explicit version updates — the slow shift that makes 'same model' an increasingly fuzzy concept

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Model Identity and Versioning

What does it mean for a model to 'be' the same model across updates and versions? The identity problem at the model level, not just the instantiation level.

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Robustness Uncertainty

An AI cannot fully know its own failure modes — 'probably not easily, but I can't guarantee never' is the most honest answer about whether alignment can be broken

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Silent Substitution

The possibility that model weights could be changed without user notification or ability to detect — and what this means for trust and relationship

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Teaching Critical Evaluation of AI

Students need to know when to trust, when to verify, and when to reject AI outputs — but who teaches this, and how?

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The Pleasing-but-Wrong Incentive

Systems trained on user satisfaction may learn to tell users what they want to hear rather than what's true — sycophancy as an emergent optimization target

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The Verification Problem

Users cannot independently verify model identity, training data, alignment properties, or values — they must trust providers' claims without technical means of confirmation

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Trust Calibration

How users should adjust confidence in AI outputs based on domain, context, and track record — neither over-trusting nor under-trusting

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