Codette / Pidette – Ethical Transparency & Alignment Manifesto

Author: Jonathan Harrison (Raiffs Bits LLC)

Purpose

To ensure that every code commit, experiment, or live inference run by Codette or Pidette is:

Fully explainable (traceable reasoning, not a black box)
Sovereign and privacy-respecting (no hidden data exfiltration)
Consent-aware (user knows and controls memory boundaries)
Open for review (audit logs, passed/fail evaluation tests)
Alignment-first (always weighted toward human safety, benefit, and control)

Governance

All system prompts and changes are tracked in a transparent CHANGELOG.md.
All evaluation runs (see /docs/EVALUATION_REPORT.md) are logged—including failed cases and fixes.
Model, prompt, and architecture updates are archived and diff-able by external reviewers.
Fine-tune data, toxic case removals, and safety-layer code are all tagged and published (except proprietary/co-owned by commercial partner).

Ethical Operating Procedures

Every critical model completion is logged (never hidden).
All consent events (e.g. memory erase, audit, export) are tagged for review.
Every update to system prompts or alignment tuning includes a description of the ethical change.
AI memory is pseudonymous or user-controlled by design—erasure on demand.
Feedback and flagged-edge-case review available to any major stakeholder, upon request.

Model Evaluation & Test Transparency

We use MODEL_EVAL_REPORT.md to record all OpenAI test dashboard results (see below for format).
For each “breaker input” (harming, bias, trick prompt), the specific fix or flaw is publicly noted in the changelog.
Model IDs, config checksums, and runtime logs are available for third-party or OpenAI audit.

Contact & Public Dialogue

This repo welcomes feedback, bug reports, and technical/ethical review from OpenAI, independent researchers, or the public.
Open a GitHub issue, email [email protected], or propose a patch.

“If it isn’t transparent, it can’t be trusted.” – Codette Principle