AI Disclosure & Transparency

1. INTRODUCTION TO OUR AI SYSTEMS

Blaze Sports Intel ("we," "our," "us") uses artificial intelligence and machine learning systems to enhance sports analytics, provide intelligent insights, and deliver predictive modeling for professional, college, and youth sports data. This disclosure explains how AI is integrated into our platform, what it can and cannot do, and your rights regarding AI-generated content.

2. AI SYSTEMS WE USE

2.1 Cloudflare Workers AI

2.2 OpenAI GPT-4 and GPT-3.5

2.3 Anthropic Claude

2.4 Google Gemini

2.5 Monte Carlo Simulation Engine

3. HOW AI IS USED ON OUR PLATFORM

3.1 AI Copilot Feature

Location: blazesportsintel.com/copilot

Our AI Copilot is an interactive conversational interface powered by Cloudflare Workers AI (Llama 3.1) that provides:

• Semantic Search: Natural language queries across our sports database
• RAG (Retrieval-Augmented Generation): AI responses grounded in verified sports data
• 3D Visualizations: Interactive Babylon.js renders of baseball fields, football formations, and basketball courts
• Conversational Analysis: Follow-up questions and contextual insights

3.2 Predictive Analytics

AI-powered predictions are used for:

• Game Outcome Projections: Win probability, score predictions
• Season Forecasts: Playoff probability, final standings projections
• Player Performance Predictions: Statistical projections based on historical trends
• Championship Probabilities: Monte Carlo-based tournament outcome modeling

3.3 Data Processing and Enhancement

AI assists with:

• Data Normalization: Standardizing formats from multiple sports data providers
• Anomaly Detection: Identifying statistical outliers and data quality issues
• Trend Identification: Pattern recognition across historical datasets
• Natural Language Summaries: Automated generation of game recaps and statistical summaries

4. AI TRAINING SOURCES

4.1 Third-Party Model Training

The AI models we use (OpenAI, Anthropic, Google, Cloudflare) were trained by their respective providers. We do not control or have access to their training datasets. According to public disclosures:

• OpenAI GPT-4: Trained on diverse internet text, books, and licensed content (cutoff: April 2023)
• Anthropic Claude: Constitutional AI training with emphasis on safety and helpfulness
• Google Gemini: Multi-modal training across text, images, video, and audio
• Cloudflare Workers AI: Open-source model deployments (Llama 3.1, BGE embeddings)

4.2 Our Proprietary Models

Blaze Intelligence's internal predictive models are trained on:

• Licensed sports data from SportsDataIO, MLB Stats API, ESPN API
• Historical game results and player statistics (2020-present)
• Perfect Game baseball data and Texas HS football statistics
• Publicly available sports reference data

5. AI LIMITATIONS AND ACCURACY

5.1 Known Limitations

AI System	Limitations	Mitigation
Large Language Models	May hallucinate facts; outdated information	RAG system; cross-reference with live data
Predictive Models	Cannot account for injuries, weather, random events	Continuous updates; confidence intervals published
Semantic Search	May misinterpret ambiguous queries	Query refinement suggestions; human review
Monte Carlo Simulations	Based on historical patterns; may not reflect current conditions	Real-time adjustments; transparent methodology

5.2 Accuracy Disclaimers

6. HUMAN OVERSIGHT PROCEDURES

6.1 Human Review Requirements

The following content types require human review before publication:

• Public-facing predictions: All championship forecasts and playoff projections reviewed by analytics team
• Youth sports data: COPPA compliance review for content involving minors
• Controversial topics: Content flagged by AI for potential issues undergoes manual review
• API integrations: Third-party data feeds validated before integration

6.2 Quality Assurance Process

1. Automated Testing: AI outputs compared against known-correct results
2. Spot Checks: Random sampling of AI-generated content for accuracy
3. User Feedback: Reporting mechanism for incorrect or misleading AI content
4. Continuous Monitoring: Real-time dashboards tracking AI performance metrics

7. BIAS MITIGATION AND FAIRNESS

7.1 Potential Bias Sources

We recognize AI systems may reflect biases present in training data:

• Historical Bias: Over-representation of certain teams or leagues in historical datasets
• Geographic Bias: Focus on Texas and Deep South region may skew recommendations
• Recency Bias: Predictive models may overweight recent performance

7.2 Mitigation Strategies

• Diverse Data Sources: Aggregation from multiple providers to reduce single-source bias
• Fairness Testing: Regular audits for demographic and geographic representation
• Transparency: Publication of model methodologies and data sources
• User Controls: Ability to adjust prediction models and filter preferences

8. USER CONTROL AND OPT-OUT OPTIONS

8.1 AI Feature Controls

Users can control AI usage through account settings:

• Disable AI Copilot: Opt-out of conversational AI features
• Prediction Preferences: Toggle predictive analytics on/off
• Data Sharing: Control whether your queries improve AI models (opt-in only)
• Personalization: Disable AI-powered recommendations

8.2 Data Deletion Rights

Per GDPR and CCPA, you may request:

• Deletion of AI Copilot conversation history
• Removal of query data used for model improvement
• Opt-out of all AI-enhanced features

Submit requests to: [email protected]

9. THIRD-PARTY AI PROVIDERS

9.1 Provider Relationships

We maintain business relationships with the following AI providers:

Provider	Service	Privacy Policy
Cloudflare, Inc.	Workers AI Platform	Cloudflare Privacy
OpenAI, L.L.C.	GPT-4 / GPT-3.5	OpenAI Privacy
Anthropic PBC	Claude AI	Anthropic Privacy
Google LLC	Gemini AI	Google Privacy

9.2 Data Sharing with AI Providers

When you use AI features, the following data may be shared with third-party AI providers:

• Query Text: Your questions and search queries
• Context Data: Sports data necessary to generate responses
• Session Metadata: Timestamps and interaction patterns (anonymized)

Not Shared: Personal identifiable information, payment details, or unrelated account data

10. AI CONTENT LABELING

10.1 Disclosure Requirements

All AI-generated content is clearly labeled with one of the following indicators:

• "AI-Generated": Content created entirely by AI without human editing
• "AI-Assisted": Content created with AI support but reviewed/edited by humans
• "Predictive Model": Statistical forecasts generated by machine learning algorithms
• "Human-Verified": AI content that has undergone manual fact-checking

10.2 Verification Indicators

Look for these visual indicators on our platform:

• 🤖 AI Icon: Marks AI-generated content
• ✓ Verified Badge: Human-reviewed AI outputs
• 📊 Model Icon: Predictive analytics content

11. CHANGES TO AI SYSTEMS

11.1 Notification of Updates

We will notify users of material changes to AI systems via:

• Email notifications to registered users
• Prominent website notice for 30 days
• Updated effective date on this disclosure page

11.2 Version Control

AI model versions and updates are tracked at: AI Disclosure Changelog

12. REPORTING AI ISSUES

12.1 Report Incorrect AI Content

12.2 AI Safety Concerns

For concerns about AI safety, bias, or ethical issues:

Email: [email protected]
Subject: "AI Safety Concern"

13. LEGAL COMPLIANCE

13.1 FTC Act Section 5 Compliance

This disclosure complies with Federal Trade Commission guidelines prohibiting deceptive acts or practices in commerce. We commit to:

• Truthful representation of AI capabilities
• Clear disclosure when AI is used
• No misleading claims about AI accuracy
• Transparent correction of AI errors

13.2 Texas Law Compliance

Per Texas Business & Commerce Code and Texas Deceptive Trade Practices Act, we ensure:

• No false or misleading AI-generated claims
• Proper disclosure of AI usage in commercial contexts
• Consumer protection in AI-driven transactions

14. CONTACT INFORMATION

15. RELATED LEGAL PAGES

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