Trust, Transparency & Credibility (TTC)

Stratogenic AI: Trust, Transparency & Credibility Framework

1. Core Trust Principles

Stratogenic AI ensures AI-driven decision-making is credible, transparent, and verifiable by adhering to three principles:

Transparency: Users must understand how AI reaches its conclusions.

Credibility: AI must back up insights with logic, data, and validation.

User Control: Users can question, refine, and improve AI outputs.

2. AI Decision Trust Metrics & Weighting

To enhance decision transparency, every AI-generated response includes a weighting breakdown:

Archetype Influence (%): How much user-selected archetype affects the decision.

Expert System Influence (%): AI-synthesized expert knowledge.

AI Synthesis (%): AI-driven pattern recognition and contextual recommendations.

Displayed to users to ensure they understand the decision-making process.

3. AI Confidence Score (Trust Indicator)

Each AI response is scored based on reliability:

90-100% ✅ Highly reliable, backed by verified data and historical accuracy.

50-89% ⚠ Moderately confident, based on available data but may require additional input.

Below 50% ❌ Low confidence, insufficient data, higher risk of inaccuracy.

The system automatically flags responses below 50% and provides suggestions to refine queries for better accuracy.

4. Contradiction & Bias Detection

To prevent misleading AI responses, the system:

Cross-checks previous AI responses for consistency.

Flags contradicting insights and explains why advice may have changed.

Alerts users of potential biases due to incomplete inputs or assumptions.

Example Display:

⚠️ Contradiction Detected:
Previous advice suggested a 10% price increase. New data suggests a 5% decrease is more effective.
 

5. Preventing Rating Bias & Troll Feedback

To ensure accurate AI improvement:

Randomized Feedback Requests: Users prompted to rate AI responses at set intervals to avoid selection bias.

One-Click Quick Ratings: Simple rating scale (✅ Useful | 🤔 Needs Work | ❌ Not Useful) to encourage engagement.

Justification for Negative Ratings: Users must provide reasons for "Not Useful" ratings to prevent abuse.

Weighting System for Feedback: Overly extreme ratings (e.g., repeated 1-star spam) are downweighted in the system.

6. API Efficiency & Cost Management (only when required)

To maintain efficiency without excessive OpenAI API costs:

Batch Process Trust Scores (every 5 queries instead of per-response).

Delay Contradiction Checks for free users (only every 10 queries).

Bias & Missing Data Alerts only trigger additional API calls when needed.

7. Final Implementation Plan

✅ Trust Score & Weighting Breakdown visible to all users.
 ✅ Contradiction & Bias Detection active across all queries.
 ✅ Feedback System optimized to prevent rating bias & troll abuse.
 ✅ API calls optimized to reduce unnecessary processing costs.

🚀 This framework transforms Stratogenic AI into an execution-proofed AI decision engine with unparalleled trust and transparency.