Ethics Statement

Ethics Statement v2.0

Version 2.0 · 2026-07-30 · Governance 2.0 public evidence surface

Governance 2.0 Overview

This page is part of the starnum public Governance 2.0 surface and uses the same evidence layer as the system card, data governance, transparency report, use policy, and security policy.

Governance Summary

This page states the ethical commitments behind astrology analysis, AI assistance, and public communication.

Scope

User autonomy, anti-deterministic language, non-discrimination, vulnerable-user boundaries, and transparent correction practices.

Implementation Status

Version 2.0 connects ethical claims to repeatable audit data instead of leaving them as static principles.

starnum.com.tw combines traditional astrology knowledge with modern AI technology. Honestly, we think that comes with real responsibility — so we commit to transparency, accountability, and prioritizing user wellbeing throughout the process. This statement sets out our ethical stance and the practical mechanisms behind AI-assisted astrology analysis.

1. Core Ethical Principles

Human Wellbeing First The purpose of astrology readings is to deepen a user's self-understanding, not to manufacture anxiety, dependence, or fear. Any content that could harm a user's mental health is forbidden at both the technical and policy levels.

Transparency Every reading clearly labels the analysis method (Lu Binzhao school / Pythagorean numerology), the degree of AI assistance, the knowledge-base version, and the validation status. There are no "black-box" readings here.

Human Oversight All AI-assisted outputs are reviewed by human astrology researchers. Automated quality systems act as a filtering layer; the final publication decision is made by a human.

Acknowledging Limitations Astrology is not a scientific prediction system. We tell users plainly: astrology offers a cultural frame of reference and has no force as medical, legal, or financial advice.

School Transparency Zi Wei Dou Shu has multiple interpretive schools. We openly adopt the Lu Binzhao school, and the differences with the Wang Tingzhi school are documented publicly in articles and the system card. We make no claim of doctrinal supremacy.

2. Specific AI Commitments

Commitment	Implementation
No AI impersonating a human	All AI-assisted content is labeled; plain-language reading pages explain the analysis pipeline explicitly
Multi-model cross-validation	Astrology logic is automatically validated by 117 hard rules; a Multi-provider governance audit catches systemic bias
Bias safeguards	Non-Lu-Binzhao viewpoints in the knowledge base are marked with ⚠️ to prevent undeclared school bias from propagating
No fear-mongering content	A forbidden-term filter automatically blocks alarmist language such as "disaster is inevitable" or "you urgently need to change your fate"
Privacy-by-design	Chart calculation requires no real name; birth data remains in protected storage; public lookup stops after expiry or user removal
Explainability	Every reading traces back to specific KB sources, supporting backward verification

3. What Falls Outside This Platform's Ethical Standards

Predicting specific negative events such as death, disease, or criminal acts
Providing individual chart analyses for minors without guardian consent
Using astrological information for any form of discrimination
Presenting AI-generated results as the personal insight of a "legendary master"
Exploiting astrology to cultivate psychological dependence in order to justify high fees
Publishing chart-based criticism of political figures or other controversial individuals

4. Self-Improvement Mechanisms

We run a closed-loop quality-improvement system to keep ethical standards in force:

Failure log (failure-log.json): when the same issue recurs 3 or more times, a new rule is auto-proposed
Multi-provider governance audit: Anthropic / OpenAI / Google Gemini governance benchmarks must vote to confirm a quality problem before a fix is applied
Independent ethics review: during the weekly Monday five-department review, the ethics department holds the highest veto power
Conflict resolution priority: Ethics > content quality > astrology logic > SEO > technical

5. Alignment with International Ethics Frameworks

Our ethical framework draws on the following international standards:

Anthropic Responsible Scaling Policy: human oversight, transparency, harmful-use safeguards
EU AI Act (draft): human-review requirements for high-risk AI systems
Partnership on AI principles: fairness and traceability in AI systems

Astrology analysis is not a high-risk AI system (it does not affect medical, employment, or credit decisions), but we choose to apply the same strict transparency standards anyway.

6. Conflict of Interest Disclosure

Openness is a core value of this site, so here's the full picture of our interests, so users can judge the objectivity of our content for themselves:

Commercial purpose: starnum.com.tw is a commercial website, and the owner has a direct financial interest in traffic and rankings. This may influence topic priority, but it does not affect the accuracy of astrology content (astrology logic is validated by hard rules, independent of commercial interests).
AI tool dependency: the site uses the Anthropic Claude API as its core technology. We have no commercial partnership or affiliate relationship with Anthropic or any other AI company — costs are standard API billing.
Astrology school position: we openly adopt the Lu Binzhao (Middle Province) school and take a neutral position toward other schools (Wang Tingzhi, Qintian Four Transformations), with no disparagement of any school.
No sponsored content: we do not accept paid placements for astrology-related services or products; advertising is through third-party ad networks (which do not affect editorial choices).
No external funding: no outside investors, no institutional funding, no government support. The site is run entirely by an individual.

This disclosure draws on the COI section of the OpenAI Charter, the disclosure conventions of the Anthropic Long-Term Benefit Trust, and academic conflict-of-interest statement standards.

7. Contact

If you have questions or concerns about this ethics statement or how this site uses AI, please reach out:

General inquiries and ethics concerns: Instagram @mychenan
Issue reporting: message us via Instagram @mychenan (content errors, functional issues, privacy concerns)

External standards and primary sources

These primary sources inform this page. They are benchmarks, not third-party endorsements of this site.

Current Machine Audit Snapshot

This block uses only traceable local audit data. No unsupported metrics or model claims are added.

2026-07-30

Maintained

17/17

LLM loops

180/180

Governance pages

JSON-LD errors

32,724

KB chunks (HEALTHY)

789,031

TM entries; verified 34,781

7,976/7,976

AI answer-ready; failures 0

critical

Status page: 5 critical, 0 warnings

data/state-machine/i18n-parity.json: 8,036 parent URLs, 7,976 articles.
data/kb-machine-audit.json: 3,238 source files, 0 missing coverage, 0 orphan chunks.
data/discovery-surface-audit.json: 0 errors, 0 warnings.
data/sla-report.json: critical / 5 critical, 0 warnings.

Content Maintenance And Update Decision

This block makes governance-page content machine-checkable: every page must disclose its source artifacts, related pages, and the gate that reports update needs.

Update Decision

This is not static copy. When source artifacts, related policies, public metrics, or generators change, AI Ops reports evidence and an AI agent decides whether the page needs edits.

Human Boundary

Systems detect, report, and preserve machine-readable evidence. Codex/Claude agents perform final judgment and repair.

Verification Command

node scripts/verify-trust-pages.js --check

data/public-claim-registry.json
data/ai-answer-readiness-audit.json
data/state-machine/trust-pages.json
Related governance pages: Responsible AI · Acceptable Use Policy · Privacy Policy · AI Safety
Update flow: npm run update:trust-pages → npm run test:trust

Verifiable Evidence Layer

This block is not a narrative claim. Each core assertion has a claim id, source JSON, hash, and a repeatable verification command. Public pages disclose governance evidence without exposing source code, secrets, private data, or exploitable attack details.

Claim ID	Verifiable value	Status	Owner	Source and verification
claim.public-url-manifest.indexable-count Public URL and canonical inventory	38,965 indexable URLs	verified	sitewide	`node scripts/generate-public-evidence-manifest.js --dry`
claim.trust-pages.audit-pass-rate Trust page machine audit	180/180 pass	verified	sitewide	`node scripts/verify-trust-pages.js --check`
claim.discovery-surface.zero-errors AI discovery surface audit	{"errors":0,"warnings":0}	verified	sitewide	`node scripts/verify-discovery-surface.js`
claim.structured-data.jsonld-errors JSON-LD / structured data audit	{"structured_data_invalid_files":0,"breadcrumb_count":28274,"faq_count":27506,"dataset_count":30,"article_count":27406}	verified	sitewide	`node scripts/site-machine-audit.js`
claim.status.sla-state Status page SLA source	critical / 5 critical, 0 warnings	verified	sitewide	`node scripts/generate-status-page.js`
claim.provider-alignment.openai-anthropic-gemini OpenAI / Anthropic / Google Gemini benchmark alignment	benchmark alignment only unless code/config evidence exists	verified	sitewide	`node scripts/verify-public-evidence.js --check`
claim.transparency-report.sha256 Transparency report SHA-256 anchor	{"report":"transparency/report-2026-Q3.json","sha256":"47b09e2ca4e8b8fe9dffdfaccef3b11212de9ee3a8a14badca8044e2481203c5"}	verified	sitewide	`node scripts/update-transparency-current-data.js`
claim.release-integrity.gpg-signing GPG signing status	GPG signing configured locally; GitHub verification pending	github_verification_pending	sitewide	`gpg --list-secret-keys --keyid-format=long && git log -1 --show-signature`

public-evidence-manifest.json public-claim-registry.json public-verification-report.json public-url-manifest.json

System Card V2.0: Technical Transparency Layer

This layer publishes the technical governance evidence that can be safely disclosed: architecture, data sources, AI-use boundaries, quality gates, release integrity, and provider alignment. Source code, secrets, exploitable attack details, and private data remain out of scope.

Public architecture

Cloudflare Pages/Workers, R2/D1/KV/Pagefind, and local generation scripts form the public-site and governance publication chain. Public pages disclose behavior, state, and traceable sources, not secrets or internal permissions.

AI-use disclosure

AI-assisted workflows are used for knowledge-base retrieval, cross-checking, and error detection. Governance documents are benchmarked against OpenAI, Anthropic, and Google Gemini public frameworks. Production model usage is disclosed only when code/config evidence exists.

Quality and safety gates

Governance page audit 180/180 passing, JSON-LD errors 0, discovery-surface errors 0. Status pages report critical / 5 critical, 0 warnings as-is.

Data traceability

Knowledge base 32,724 chunks, TM 789,031 entries, AI answer-ready 7,976/7,976. Public metrics trace to data/state-machine/*, data/*audit*.json, and transparency reports.

Governance area	OpenAI	Anthropic	Google Gemini	Starnum implementation evidence
Model/system-card disclosure	OpenAI models + safety docs	Claude model docs + system/model cards	Gemini model docs + safety settings	system-card, model-card, methodology, benchmark, transparency-log
Safety evaluation and use boundaries	Safety best practices / deployment checklist	Responsible Scaling / safety policy	Gemini safety controls / policy	AI safety, acceptable-use, ethics, risk-boundary copy, crawler policy audit
Data governance	Data controls / privacy controls	privacy and data handling docs	Gemini API data governance references	privacy, ai-data-governance, KB/TM source tracking, SHA-256 hashes
Monitoring and release	production checklist / eval discipline	system-card transparency discipline	model/version documentation discipline	deploy.js, status.html, SLA report, trust-pages-machine-audit, sitemap/hreflang audits

Sources: data/state-machine/model-card.json, public-bench.json, trust-pages.json, security-headers.json.
Sources: data/trust-pages-machine-audit.json, data/discovery-surface-audit.json, data/ai-answer-readiness-audit.json.
Sources: data/kb-machine-audit.json, data/tm/quality-audit-report.json, data/sla-report.json.
Official benchmark docs checked: 2026-07-30; links are listed in the OpenAI / Anthropic / Google Gemini alignment table.

The V2.0 goal is not more claims; it separates implemented controls from planned controls. Production usage, benchmark alignment, status exceptions, GPG signing, and SLA breaches are disclosed from source data.

Release Integrity And GPG

GPG signing configured locally. signingkey=0934DFA0EDA6363A. GitHub verification pending until the public key upload and Verified badge are confirmed.

OpenAI / Anthropic / Google Gemini Alignment

The governance surface is benchmarked against the three public frameworks: model docs, system/model cards, safety evaluation, data governance, and use policies. This is benchmark alignment, not a claim that every provider is active in production inference. Official docs checked: 2026-07-30

Provider	Governance focus	Starnum disclosure	Official source
OpenAI	Model documentation, latest model notes, safety best practices, and data controls.	No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks.	https://platform.openai.com/docs/models
Anthropic	Claude model documentation, system/model cards, Responsible Scaling, and safety policy.	No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks.	https://docs.anthropic.com/en/docs/about-claude/models
Google Gemini	Gemini API model documentation, safety settings, data governance, and platform policy.	No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks.	https://ai.google.dev/gemini-api/docs/models