Reputation & Rating Algorithm
Civic Commerce Trust Engine — Transparent, Evidence-Based, Anti-Manipulation Draft v0.1
Preamble
The Freedom Stack Reputation System helps people identify trustworthy businesses, workers, institutions, and contributors through:
Evidence • Transparency • Fairness • Accountability • Anti-Corruption
Foundational rule: Reputation must be earned, verifiable, appealable, and difficult to game.
Goal: honest behavior compounds trust; exploitation degrades it; wealth alone cannot buy legitimacy.
Section I — Design Principles
- Reward verified ethical behavior
- Penalize fraud and abuse
- Resist fake reviews
- Resist brigading
- Preserve due process
- Be explainable
- Encourage improvement
- Distinguish severity from popularity
Section II — Entity Types
Businesses, individuals, workers, educators, health providers, governance delegates, local nodes.
Section III — Multi-Dimensional Score
Total Trust Score =
Labor + Transparency + Product Integrity + Privacy + Community + Governance + Audit
| Category | Measures |
|---|---|
| Labor Fairness | Wage reliability, conditions, retention, verified grievances, mediation outcomes |
| Transparency | Pricing clarity, ownership disclosure, complaint handling, audit participation |
| Product / Service Integrity | Delivery accuracy, warranty behavior, fraud incidents, quality verification |
| Privacy Ethics | Data minimization, security, consent, surveillance avoidance |
| Community Contribution | Local hiring, apprenticeship, education support, mutual aid |
| Governance Conduct | Rule compliance, charter adherence, appeals responsiveness |
| Audit Reliability | External verification, random audits, incident response |
Section IV — Score Weighting (Default)
| Category | Weight |
|---|---|
| Labor | 20% |
| Transparency | 15% |
| Product Integrity | 20% |
| Privacy | 10% |
| Community | 10% |
| Governance | 10% |
| Audit | 15% |
Sector overrides: healthcare → higher ethics + safety; software → higher privacy + integrity; food → higher labor + quality.
Section V — Review Input Tiers
| Level | Source |
|---|---|
| 1 | Verified transaction review |
| 2 | Verified employee review |
| 3 | Auditor review |
| 4 | Random inspection |
| 5 | Legal or severe incident review |
Trust hierarchy: Auditor > Worker > Verified Customer > Anonymous Public
Section VI — Anti-Gaming
Threats: fake reviews, purchased reviews, competitor sabotage, political attacks, bot spam, coordinated brigades.
Technical countermeasures: verified interaction proofs, identity uniqueness, rate limiting, behavioral anomaly detection, temporal review weighting, reviewer reputation.
Social countermeasures: public dispute channels, appeals, audit triggers, fraud penalties.
Section VII — Reviewer Reputation
Reviewers receive credibility scores based on accuracy, historical validity, false claim rate, verification level. Not all reviews are equal.
Section VIII — Time Decay
| Window | Weight |
|---|---|
| Recent 12 months | 60% |
| Past 1–3 years | 30% |
| Older | 10% |
Allows reform and redemption.
Section IX — Severe Violation Model
Severity > Volume. One severe verified abuse can outweigh many superficial positives.
Examples: wage theft, fraud, corruption, abuse, safety negligence.
Section X — Appeals & Due Process
All entities may contest claims, submit evidence, request audits, seek remediation review.
No irreversible digital exile without process.
Section XI — Reputation Badges
Worker Trusted • Privacy Respecting • Community Builder • Transparent Enterprise • Education Ally • Fair Labor Certified
Section XII — Red Flags
Repeated labor disputes, audit refusal, fraud claims, high refund conflict, governance violations.
Section XIII — Score Presentation
Show: overall trust score, category breakdown, audit status, review count quality, risk flags, improvement trend.
Better than a single "4.2 stars".
Section XIV — Reputation Recovery
Requires corrective action, restitution, verified reform, audit passage. Goal: justice, not permanent destruction.
Section XV — Local Node Customization
Local communities may adjust weightings, cultural standards, sector emphasis. Core anti-fraud and rights standards remain universal.
Section XVI — AI / Agent Support
Agents can: detect anomalies, flag manipulation, summarize patterns, identify fraud clusters.
Agents cannot: finalize punishments, secretly suppress.
Section XVII — Reputation Portability
Users/businesses can carry certifications, audit history, education credentials, verified trust across federation.
Section XVIII — Failure Modes
Risks: social credit dystopia, ideological scoring, reviewer cartels, hidden algorithm bias.
Mandatory safeguards: open algorithm transparency, explainability, appeals, independent oversight, forkability.
Sample Scorecard
Business: 87/100
Labor: 91
Transparency: 84
Integrity: 89
Privacy: 72
Community: 88
Audit: Verified Gold
Risk: Moderate (privacy policy weakness)
Trend: Improving
MVP Build Requirements
- Identity-linked reviews
- Verified transaction system
- Worker review module
- Audit engine
- Appeals dashboard
- Public trust profile
Primary reputation law: "Reputation measures conduct, not ideology."