Really appreciated this intro—your concerns map closely to mine. I’m Joshua; I work at the intersection of decentralized governance, legal advocacy (asset-forfeiture/victim rights), and AI tooling. I’m rebuilding capacity after post-Cushing’s recovery, so I bias toward small, structured discussions and concrete next steps.
A few quick points of alignment + questions:
Data governance / consent — I’m interested in mechanisms that make “consented use” provable and revocable (e.g., per-subject attestations with audit trails). Have you found any workable designs that avoid dark patterns and keep enforcement costs low?
Transparent, auditable inference — Beyond reproducible training, I’m looking at “reproducible answers”: pinning model snapshots + prompts + retrieval corpora so third parties can re-run specific claims. Are you pursuing anything like signed inference receipts or verifiable retrieval logs?
Decentralized incentives — My bias is that you only get durable transparency if someone pays for it. Have your proofs of concept explored staking/penalty mechanisms (e.g., slashing for unverifiable outputs or misdeclared data provenance) rather than just “open” licenses?
Safety vs. capture — Curious how you’re thinking about preventing governance from collapsing into cartelized gatekeeping while still stopping obvious abuse. Any governance schematics you’d be willing to share?
If you’re up for it, I’d love a short, agenda-driven chat (30–40 min). I can send a 1-pager on the whistleblowing/insurance governance work I’m doing (decentralized claim-grading + anti-collusion incentives) and would be happy to read one of your POCs in exchange. Prefer a low-noise venue or a quick video call.
Either way, if you can point me to the best thread/paper that captures your current architecture, I’ll read it before we talk.
Really appreciated this intro—your concerns map closely to mine. I’m Joshua; I work at the intersection of decentralized governance, legal advocacy (asset-forfeiture/victim rights), and AI tooling. I’m rebuilding capacity after post-Cushing’s recovery, so I bias toward small, structured discussions and concrete next steps.
A few quick points of alignment + questions:
Data governance / consent — I’m interested in mechanisms that make “consented use” provable and revocable (e.g., per-subject attestations with audit trails). Have you found any workable designs that avoid dark patterns and keep enforcement costs low?
Transparent, auditable inference — Beyond reproducible training, I’m looking at “reproducible answers”: pinning model snapshots + prompts + retrieval corpora so third parties can re-run specific claims. Are you pursuing anything like signed inference receipts or verifiable retrieval logs?
Decentralized incentives — My bias is that you only get durable transparency if someone pays for it. Have your proofs of concept explored staking/penalty mechanisms (e.g., slashing for unverifiable outputs or misdeclared data provenance) rather than just “open” licenses?
Safety vs. capture — Curious how you’re thinking about preventing governance from collapsing into cartelized gatekeeping while still stopping obvious abuse. Any governance schematics you’d be willing to share?
If you’re up for it, I’d love a short, agenda-driven chat (30–40 min). I can send a 1-pager on the whistleblowing/insurance governance work I’m doing (decentralized claim-grading + anti-collusion incentives) and would be happy to read one of your POCs in exchange. Prefer a low-noise venue or a quick video call.
Either way, if you can point me to the best thread/paper that captures your current architecture, I’ll read it before we talk.