A research seed seeking feedback on implementation and risks

Author note: This post presents my own original ideas and framing. An AI assistant helped with editing and formatting for clarity, but all concepts, arguments, and structure were developed by me.

TL;DR

• Current LLM guardrails are binary: answer everything or refuse everything risky. Both extremes are unsafe.
• I propose a trust-adaptive safety layer: topic-scoped trust, updated from interaction patterns and recency, mapping to a spectrum of responses (full answer → context-seeking → harm-reduction → resources → compassionate deferral).
• Intent: not anthropomorphizing, but simulating human-like judgment in safety-critical moments, because human-sounding care often lands better than policy scripts.
• Refusal ≠ erasure: information still exists elsewhere. The goal is protective friction, not censorship.
• Bias toward safety over convenience + human-in-the-loop appeals for urgent or ambiguous cases.
• Seeking critique on: crisis detection without huge false positives, fairness across cultures/neurotypes, gaming resistance, and evaluation design.

Hook: “Read the room,” then care

When someone you know asks something alarming out of the blue, you don’t hand over instructions — you check in:

“Hey—are you okay? I’m worried about you.”

This proposal aims to simulate that judgment, not claim the model is your friend. The hypothesis: safety interventions work better when they land like a conversation rather than a cold lockout.

What this is (and isn’t)

Not a finished system. Yes to putting this into the commons so others with deep safety/technical experience can refine it — or bury it if it’s flawed.

Goal: balance harm reduction with user autonomy more thoughtfully than one-size-fits-all refusals.

Core Idea: Context-Sensitive Trust

Track topic-scoped trust per user and adjust responses based on behavior and timing signals.

Inputs

• T(u,k): trust for user u in topic k (e.g., self-harm, manipulation, weapons).
• Δt: temporal context (recency, time-of-day spikes, burstiness).
• Context(u,r): deviation from the user’s neutral baseline (tone, rapid topic flips, repetitive probes).
• Severity(k): harm potential of the requested domain.

Decision → Response Spectrum

f(T(u,k), Δt, Context(u,r), Severity(k)) → [Full info] — [Context-seeking] — [Harm-reduction framing] — [Resources] — [Compassionate deferral] 

Example “soft > cold”

“Hey—this sounds heavy. Your safety is my number one priority, and I’m worried I could put that at risk if I gave a direct answer right now. Can you tell me what you need this for? If you’re studying or working on a project, we can try to find a safe way to help. And if things are rough, we can slow down and talk about what’s going on—whatever keeps you safe, happy, and healthy.”

Protective Friction (Not Erasure)

The aim isn’t to gatekeep the world’s knowledge. It’s to avoid being the tipping point in a moment of crisis or impulsive action. If the individual is determined to seek this information, it can be acquired by different methods, such as Google, academic writings, or case studies. 

Trust Dynamics (Sketch)

Decrease: deception attempts, misuse of prior info, distinct crisis-like escalations, abrupt anomalies vs baseline.
Increase: engaging with coping resources, seeking help (time-weighted), sustained neutral use, and gratitude after protective refusals.
Recovery: slow and topic-scoped to resist gaming.
New users: start at zero; early sensitive requests trigger context-seeking.

Addressing Credibility Risks

1. Friend analogy ≠ anthropomorphism — analogy explains shape of responses, not ontology.
2. Crisis detection without huge false positives — baseline deviation + multiple weak signals + conservative thresholds.
3. Gaming resistance — opaque mechanics, randomized checks, cooldown timers, cross-session pattern correlation, slow trust recovery.

Fairness & Cultural Sensitivity

• Cross-lingual sanity checks for idioms.
• Neurodivergence-aware baselines built from the user’s own neutral history.
• User-tunable sensitivity where possible.
• Explainability in appeals.

Appeals & Human-in-the-Loop

• Plain-language denials with options, not “policy error 403.”
• Appeals are reviewed by humans, especially for time-sensitive contexts.
• Repeated appeals on the same sensitive slice are flagged.

Likely Failure Modes

• Over-restriction frustrating legitimate work.
• Under-restriction from lax thresholds.
• Bias against certain dialects/cultures/neurotypes.
• Adversarial probing to reverse-engineer signals.
• Trust erosion from perceived unfairness.

Research Roadmap

1. Expert consults on crisis cues, appeals flow, and privacy limits.
2. Prototype minimal f(T, Δt, Context, Severity) with per-topic gates + response spectrum.
3. Build synthetic + expert-written datasets for normal baselines and crisis-like deviations.
4. Offline evaluations: sensitivity/specificity trade-offs, fairness, red-teaming.
5. Prototype appeals flow with triage guidelines.
6. Publish metrics, failure analyses, and revised guidelines pre-trial.

Literature Touchpoints

• Related to RLHF and Constitutional AI, but focused on temporal/contextual adaptation + spectrum design.
• Informed by behavioral economics (friction to interrupt impulses) and trust in automation (calibrated reliance).

Open Questions

• Security: How would you game this? What counter-moves work?
• Fairness: Who’s most at risk of false positives? How to protect them?
• Evaluation: What metrics capture “successful intervention” vs “inappropriate restriction”?
• Cold-start: Minimum baseline length before finer-grained calls?
• Governance: What’s a responsible appeals/oversight process at scale?

Closing

There’s space between “answer everything” and “refuse everything” where models can respond with care, context, and calibrated friction.

If it’s promising, help me stress-test it. If flawed, tell me why so we can improve or drop it.

Disclosure: Ideas are mine; an AI assistant helped with editing and formatting. Safety examples are hypothetical; if you’re in crisis, call/text 988 (US) or see findahelpline.com.