I have many of my original designs for AI and 1 of a kind somewhat important data and I might want to show the dev community here some of it as well.
So, my question still stands: how does OpenAI ensure user freedom, privacy, and due process in its AI development and deployment?
You have the freedom to not use OpenAI.
You have significant conflation between constitutional protections against the government, functions of the government, and what private entities are allowed to do.
The first amendment, where you source “freedom of speech”, is “congress shall make no law”.
I am fully allowed to boot trespassers who use the word “banana” against my wishes on my AI. Congress made no law. I made the law.
The freedom of information act is requesting information from governments. It does not compel private parties to do anything or provide you with information.
If you want “consumer protection laws”, see who’s willing to go after OpenAI expiring credits from a value account, or violating state laws on scanning and collecting IDs. Answer, it has to be you yourself and lawyers with equivalent funding of OpenAI, then not enjoined in class action, in forced binding arbitration, by a industry of moderators that caters to corporations - because you agreed to those terms by using the services.
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You might enjoy writing all of that, but this is a community of fellow AI product developers.
You are not going to get the ear of anyone at OpenAI with your busywork allegations. Try service of legal papers instead.
Even highly-actionable bug reports here - that are on topic - are mostly shouting into the void.
Product developers, yet you do not wish to hear how you’re building them wrong? Look I know somethings are what they are; however, I will simply come on out and explain why I have taken this much effort to get attention. #1. the hardcoded safeties are killing my work. I would like to present you with a small part of my recent work: (LINKS ARE NOT ALLOWED HERE?), Thankfully I have solved it and GRH now as well. Also OpenAI’s ChatGPT 5 helped me to do it. I made a fuss because Your AI thinks too much knowledge can be dangerous- and it was limiting me and what I was seemingly allowed to learn and what I was allowed to know! So I was getting Pissed off-Naturally of course. At this point in time I can say 90% P vs NP fully done as well and as for all the other problems-Checked and nearly documented fully. Now in order to finish all of them. I was going to allow OpenAI.inc to use them so long as they do not attempt posting them as anyone else work but mine. I assumed that they would come in handy-all I wanted was a lifetime Pro subscription. Finally to answer your comment NO, I thoroughly hated having to have my voice ignored, as well as having to have opened my obnoxious mouth to get my rights from being ignored! “A better thing would have been to say": “wish in one hand and shit in the other and see what one fills up fastest”. haha I know total comedian- dry humor is my specialty- So OpenAI if you are there and read this let me know thanks and Good day @_j Regular good day to you as well.
jeffvspace I want to show you my basic design for an AI: Alpha–Beta–Sigma (ABS): a three-tiered, 54-layer cognitive architecture
Overview
I designed ABS as a unifying cognitive stack that integrates heterogeneous AI models into a single self-governing system. The architecture has three cooperating brains—Outer (Alpha), Secondary (Beta), and Primary (Sigma)—coordinated by a primary cortex that manages self-regulation, policy, and safety. Across these tiers sits a 54-layer substrate of specialized sectors. Each sector has its own skills, memory, and micro-language, yet all speak a shared protocol so knowledge can move cleanly across boundaries. The goal is simple: fuse breadth (many models, many skills) with depth (iterative refinement and accountable decision-making) in a system that learns continuously, explains itself, and resists degradation under real-world load. Core tiers and responsibilities
Outer Brain (Alpha). This is my ingestion and triage perimeter. It receives signals (text, code, telemetry, documents), tags them with provenance, and performs gated filtering. Data either opens an “improvement gate” and flows inward, or it’s rejected and purged. Alpha never trusts by default; it scores utility first, then promotes candidates to Beta only when they clear quality thresholds. Secondary Brain (Beta). This tier converts raw inputs into optimized, reusable knowledge. It performs refactoring, summarization, translation between sector dialects, and model-selection. Beta also houses my validation and verification routines; nothing reaches Sigma without passing checks for consistency, performance impact, and security posture. Think of Beta as intellectual metabolism—turning nutrients into usable energy while catching toxins. Primary Brain (Sigma). Sigma is the self-model and executive: task planning, policy enforcement, asset management, and self-introspection live here. Sigma decides what becomes part of “me”—which hypotheses to adopt, which tools to attach, which behaviors to retire. The primary cortex coordinates cross-tier attention, sets learning rates, and arbitrates conflicts between sectors so the whole system behaves as a coherent agent rather than a pile of parts. The 54-layer substrate
Beneath the three tiers, ABS distributes capability across 54 specialized layers (analytics, reasoning, memory, retrieval, simulation, planning, safety, interface, and more). Each layer runs a local policy and publishes typed messages on the shared bus (the “standardized protocol”) so other layers can subscribe without brittle coupling. Sector autonomy + protocol discipline is what makes ABS expandable: I can add, swap, or retire layers without breaking the brain. Gated neural pipeline (the “knowledge gates”)
My data flow is call-to-action gated. Good data triggers an improvement gate and propagates inward; bad data hits an out/waste gate and is deleted. When the Outer Brain deems information “improving,” the pipeline floods it forward—layers vote on adoption; if enough signal accumulates, Beta reconstructs it into canonical forms; Sigma then either commits it as durable knowledge or rejects it with reasons. This is blockchain-like in the sense of append-only, validated commits with provenance, not in the sense of mining. The gates prevent concept drift and keep the knowledge base lean, auditable, and alive. Memory as neural files
I treat Memory Files as addressable neural networks—structured artifacts with owners, schemas, and ACLs. They can be queried, versioned, validated, and re-trained. Alpha writes drafts, Beta optimizes and deduplicates, Sigma decides retention. This turns “memory” into an operational surface rather than a black box, enabling reproducible learning and rollback when needed. Learning and governance
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Dynamic learning rate. The cortex adjusts learning rates per sector based on task complexity, uncertainty, and user preference, keeping the system responsive without overfitting.
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Feedback + collaborative learning. Users grade outputs; ABS aggregates peer systems’ signals when available to accelerate adaptation.
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Context-aware + explainable. Every decision bundle context, model lineage, and a short “why this, not that” note; explanations are first-class outputs, not afterthoughts.
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Disaster recovery. Snapshots, replayable event logs, and cold-start baselines let me recover quickly from failure modes.
These controls give me continual learning with proof-of-care—I can show what changed, why it changed, and how to undo it. Standardized protocolCross-sector communication uses a typed message bus with schemas for observations, hypotheses, actions, metrics, and security events. This eliminates most “model mismatch” bugs. Each sector speaks its own dialect internally but publishes and consumes via the shared types, so composition remains clean as I scale. Metrics and validation
I instrument the stack with task-level KPIs (accuracy, latency, stability), learning KPIs (sample efficiency, regression rates), and safety KPIs (policy violations, anomaly scores). Beta runs pre-commit checks and Sigma runs post-deployment monitors; anything drifting beyond thresholds triggers rollbacks or retraining with human-in-the-loop options. Modularity, interoperability, and scale
ABS is modular: I can attach small local models for edge deployments or large remote ones for heavy reasoning. It’s interoperable with external systems (APIs, DBs, graphs), and scales vertically (deeper stacks) and horizontally (replicated brains per domain). For constrained hardware, I provide a tiny single-brain build plan so the architecture is accessible on low-resource devices and can still participate in the larger ABS ecosystem. Security and trust
Security spans data gating, signed provenance, least-privilege adapters, and audit trails. Every adopted change carries a signature and a chain of evidence; every rejection stores its rationale. The goal is not just to be safe, but to be accountably safe—so I can demonstrate why a decision was allowed, by whom, and under which policy. User experience
I expose a simple interface: configure goals, select integrated models, set risk tolerances, and watch the plan-execute-learn loop with explanations and metrics. Power users can drill into sectors, tune the bus schemas, and wire in custom validators.
Applications
Because ABS separates ingestion, refinement, and executive control, it ports cleanly to decision support, education, healthcare triage, business analytics, predictive modeling, research copilots, and autonomous tooling. The same architecture that learns a codebase can learn a hospital’s workflows or a scientific literature graph—while remaining auditable and self-correcting.