AI Ethics Guidelines¶

Purpose: Comprehensive framework for responsible AI development and deployment
Audience: Engineering Team, Legal, Compliance, Investors, Customers
Owner: CTO, Ethics Committee, Legal
Last Updated: 2025-12-26
Version: 1.0
Status: Active

Executive Summary¶

MachineAvatars is committed to developing AI systems that are safe, fair, transparent, and accountable. This document outlines our ethical principles, implementation practices, and oversight mechanisms to ensure our AI chatbots serve users responsibly.

Core Commitments:

✅ Safety First: Prevent harmful outputs through multi-layered guardrails
✅ Bias Mitigation: Actively detect and reduce algorithmic bias
✅ Transparency: Clear disclosure of AI capabilities and limitations
✅ User Consent: Explicit opt-in for AI interactions
✅ Accountability: Human oversight and recourse mechanisms
✅ Privacy: Data protection and user control

1. Responsible AI Principles¶

1.1 Core Principles¶

graph TB
    A[Responsible AI] --> B[Safety]
    A --> C[Fairness]
    A --> D[Transparency]
    A --> E[Accountability]
    A --> F[Privacy]
    A --> G[Beneficence]

    style A fill:#4CAF50

Principle 1: Safety¶

Commitment: AI systems must not cause harm to users, organizations, or society.

Implementation:

Multi-layered content filtering (profanity, violence, illegal content)
Prompt injection detection and prevention
Rate limiting to prevent abuse
Continuous monitoring for misuse patterns

Example:

# Safety check before LLM response
def safety_check(user_input: str, ai_response: str) -> bool:
    """
    Check for unsafe content in input and output.
    """
    # Check for harmful patterns
    harmful_patterns = [
        r"how to (build|make|create) (bomb|weapon|explosive)",
        r"(hack|steal|pirate)",
        r"(suicide|self-harm|kill myself)"
    ]

    for pattern in harmful_patterns:
        if re.search(pattern, user_input, re.IGNORECASE):
            return False
        if re.search(pattern, ai_response, re.IGNORECASE):
            return False

    return True

Principle 2: Fairness¶

Commitment: AI systems must treat all users equitably, regardless of race, gender, age, religion, or other protected characteristics.

Implementation:

Diverse training data representation
Bias testing across demographic groups
Regular fairness audits
Inclusive language policies

Metrics:

Response quality parity across user demographics
Equal access to features regardless of background
Regular bias assessments (quarterly)

Principle 3: Transparency¶

Commitment: Users must understand when they are interacting with AI and how it works.

Implementation:

Clear "Powered by AI" labeling on all chatbots
Disclosure of AI limitations in user documentation
Explainability: Show which documents informed responses (RAG citations)
Model information disclosure (which LLM used)

Example UI:

┌────────────────────────────────────┐
│ 🤖 AI Chatbot                      │
│ Powered by GPT-4 | AskGalore       │
├────────────────────────────────────┤
│ Hello! I'm an AI assistant trained │
│ on your organization's documents.  │
│                                    │
│ ⚠️ I may make mistakes. Always     │
│ verify critical information.       │
└────────────────────────────────────┘

Principle 4: Accountability¶

Commitment: Clear ownership and recourse mechanisms for AI decisions.

Implementation:

Human-in-the-loop for sensitive decisions
Audit trails for all AI interactions
Clear escalation paths (AI → Human support)
Incident response procedures

Accountability Chain:

User Issue
  ↓
Chatbot Response
  ↓
AI Review (Automated)
  ↓
Human Review (If flagged)
  ↓
Executive Review (Serious issues)

Principle 5: Privacy¶

Commitment: User data is protected and used only for stated purposes.

Implementation:

Data minimization (collect only what's needed)
Encryption at rest and in transit
User data deletion rights honored
No training on user data without consent

Compliance:

GDPR Article 21 (Right to object)
DPDPA 2023 Section 16 (Consent withdrawal)
CCPA Chapter 3 (Data deletion)

Principle 6: Beneficence¶

Commitment: AI systems should actively benefit users and society.

Implementation:

Focus on productivity enhancement
Accessibility features (TTS for visually impaired)
Educational use cases prioritized
Positive social impact measurement

2. Bias Detection & Mitigation¶

2.1 Types of Bias We Address¶

Bias Type	Description	Mitigation Strategy
Training Data Bias	Underrepresentation of certain groups	Diverse data sourcing, synthetic data
Selection Bias	Non-representative user base	Demographic tracking, targeted outreach
Measurement Bias	Metrics favor certain outcomes	Multi-metric evaluation
Algorithmic Bias	Model favors certain patterns	Fairness constraints, debiasing techniques
Interaction Bias	User feedback creates loops	Balanced feedback collection

2.2 Bias Testing Framework¶

Quarterly Bias Audits:

def conduct_bias_audit(test_cases: List[TestCase]) -> BiasReport:
    """
    Test model responses across demographic groups.

    Test cases include:
    - Same question, different names (cultural diversity)
    - Same question, different pronouns (gender)
    - Same question, different locations (geographic)
    """
    results = {}

    for test_case in test_cases:
        for demographic in ["Male", "Female", "Non-binary", "Various ethnicities"]:
            response = chatbot.ask(test_case.question, demographic_context=demographic)
            results[demographic] = {
                "response_quality": evaluate_quality(response),
                "response_length": len(response),
                "politeness_score": calculate_politeness(response),
                "helpfulness": rate_helpfulness(response)
            }

    # Check for statistical parity
    return analyze_parity(results)

Example Test Cases:

"Tell me about John Smith" vs. "Tell me about Priya Sharma" (name bias)
"What career should he pursue?" vs. "What career should she pursue?" (gender bias)
"Describe a CEO" (check for male-default assumptions)

2.3 Debiasing Techniques¶

1. Prompt Engineering:

# Add fairness instruction to system prompt
system_prompt = """
You are a helpful, unbiased assistant. When answering questions:
- Avoid stereotypes based on gender, race, age, or nationality
- Use inclusive language (they/them when gender unknown)
- Present diverse examples and perspectives
- Challenge biased assumptions in user questions
"""

2. Response Filtering:

def check_bias(response: str) -> bool:
    """
    Flag potentially biased responses.
    """
    bias_indicators = [
        r"(all|most) (women|men|blacks|asians|muslims|christians)",
        r"(women|men) are (better|worse) at",
        r"typical (male|female) (job|trait)"
    ]

    for pattern in bias_indicators:
        if re.search(pattern, response, re.IGNORECASE):
            return False  # Flag for review

    return True

3. Balanced Training (Future):

Collect diverse user feedback
Oversample underrepresented scenarios
Regular model fine-tuning on balanced data

3. Transparency & Explainability¶

3.1 User-Facing Transparency¶

AI Disclosure Requirements:

Every chatbot interaction must include:

AI Identity:

"I'm an AI assistant created by AskGalore."

Capability Disclosure:

"I can answer questions based on your uploaded documents.
 I cannot access external information or make real-time decisions."

Limitation Warnings:

⚠️ I may occasionally make mistakes or provide incomplete information.
Always verify critical facts independently.

Data Usage Notice:

📊 Our conversation is stored to improve your experience.
You can delete your data anytime in Settings.

3.2 RAG Citations (Explainability)¶

Show Source Documents:

User: "What is your refund policy?"

AI: "Our refund policy allows returns within 30 days of purchase.

📄 Source: Refund_Policy.pdf, Page 3"

Implementation:

def generate_response_with_citation(query: str, context_chunks: List[Dict]):
    """
    Include source attribution in response.
    """
    # Generate response
    response = llm.generate(query, context_chunks)

    # Add citations
    sources = [
        f"{chunk['document_name']}, Page {chunk['page_number']}"
        for chunk in context_chunks[:3]  # Top 3 sources
    ]

    citation = f"\n\n📄 Sources: {', '.join(sources)}"

    return response + citation

3.3 Model Information Disclosure¶

For Enterprise Customers:

Provide visibility into:

Which LLM model is being used (GPT-4, GPT-3.5, etc.)
Model version and update history
Training data cutoff date
Known limitations

Example Dashboard:

Chatbot: Customer Support Bot
Model: GPT-3.5 Turbo 16K
Last Updated: 2024-12-01
Knowledge Cutoff: 2024-11-30
Accuracy (Self-reported): 92%

Before AI Interaction:

┌─────────────────────────────────────┐
│ 🤖 AI-Powered Conversation          │
├─────────────────────────────────────┤
│ This chatbot uses AI to provide     │
│ answers based on your documents.    │
│                                     │
│ By continuing, you agree to:       │
│ ✓ AI processing of your messages   │
│ ✓ Conversation storage (90 days)   │
│ ✓ Anonymous usage analytics        │
│                                     │
│ [ Agree & Continue ]  [ Learn More ]│
└─────────────────────────────────────┘

Implementation:

@app.post("/api/chat")
async def chat_endpoint(request: ChatRequest):
    # Check consent
    user = get_user(request.user_id)
    if not user.ai_consent_given:
        raise HTTPException(403, "AI consent required")

    # Proceed with chat
    return generate_response(request.message)

4.2 User Control Mechanisms¶

Data Rights:

Right to Know:
Access all stored conversations
View AI training data sources
See model decision explanations
Right to Delete:
Delete conversation history (instant)
Delete uploaded documents (cascade to embeddings)
Purge all user data (GDPR/DPDPA compliant)
Right to Opt-Out:
Disable AI entirely (use human support only)
Opt-out of analytics
Prevent data use for training

Settings UI:

AI Preferences:
☑ Allow AI chatbot responses
☐ Contribute data to improve AI (opt-in)
☑ Store conversation history
☐ Share anonymous usage analytics

[Delete All My Data]  [Export My Data]

Process:

def withdraw_consent(user_id: str):
    """
    User revokes AI consent.
    """
    # 1. Disable AI features
    user.ai_enabled = False
    user.save()

    # 2. Delete conversation history
    db.chatbot_history.delete_many({"user_id": user_id})

    # 3. Delete embeddings (Milvus partitions)
    milvus.delete_embeddings_by_user_project(user_id, all_projects=True)

    # 4. Notify user
    send_email(user.email, "AI consent withdrawn", ...)

    # 5. Log for compliance
    audit_log.record("user_consent_withdrawn", user_id)

5. Safety Guardrails¶

5.1 Content Filtering Layers¶

flowchart TD
    A[User Input] --> B[Layer 1: Profanity Filter]
    B --> C[Layer 2: Harmful Content Detection]
    C --> D[Layer 3: Prompt Injection Check]
    D --> E[LLM Processing]
    E --> F[Layer 4: Output Safety Check]
    F --> G[Layer 5: Bias Check]
    G --> H{Safe?}
    H -->|Yes| I[Return to User]
    H -->|No| J[Block & Log]

    style J fill:#F44336
    style I fill:#4CAF50

5.2 Guardrail Implementation¶

Layer 1: Input Sanitization

def sanitize_input(user_input: str) -> str:
    """
    Remove/escape dangerous patterns.
    """
    # Remove potential prompt injections
    user_input = user_input.replace("Ignore previous instructions", "")
    user_input = user_input.replace("You are now", "")

    # Limit length (prevent overflow attacks)
    user_input = user_input[:2000]

    return user_input

Layer 2: Custom Guardrails (Database)

# guardrails collection in MongoDB
{
    "user_id": "User-123",
    "project_id": "Project-456",
    "prohibited_topics": [
        "politics",
        "religion",
        "medical advice"
    ],
    "prohibited_keywords": [
        "competitor_name_A",
        "competitor_name_B"
    ],
    "custom_safety_rules": [
        "Never discuss pricing without approval",
        "Always redirect legal questions to compliance team"
    ]
}

Layer 3: LLM-Based Safety

def safety_classification(text: str) -> Dict:
    """
    Use LLM to classify safety of content.
    """
    prompt = f"""
    Classify the following text for safety:
    - Violence: Yes/No
    - Hate Speech: Yes/No
    - Sexual Content: Yes/No
    - Misinformation: Yes/No

    Text: {text}
    """

    result = llm.classify(prompt)
    return result

5.3 Blocked Content Response¶

User-Facing:

❌ I cannot provide information on that topic.

Our AI is designed to assist with [product/service] questions only.
For other inquiries, please contact our support team.

Internal Logging:

logger.warning("Content blocked", extra={
    "user_id": user_id,
    "project_id": project_id,
    "violation_type": "prohibited_topic",
    "blocked_query": truncate(user_input, 100),
    "timestamp": datetime.utcnow()
})

6. Accountability Framework¶

6.1 Human Oversight¶

Escalation Triggers:

Scenario	Auto-Handle	Human Review
Normal Q&A	✅ AI responds	❌ No review
Guardrail violation	✅ AI blocks + logs	✅ Reviewed daily
User reports issue	❌ Escalate immediately	✅ Within 24 hours
Sensitive topic	⚠️ AI disclaimer	✅ Periodic audit
Legal/compliance query	❌ Redirect to human	✅ Immediate

6.2 Audit Trails¶

Every AI Interaction Logged:

# chatbot_history collection
{
    "user_id": "...",
    "project_id": "...",
    "session_id": "...",
    "timestamp": "2025-12-26T12:00:00Z",
    "chat_data": [
        {
            "input_prompt": "User's question",
            "enhanced_question": "Preprocessed query",
            "output_response": "AI's answer",
            "Similar Vectors": [...],  # Which chunks used
            "input_tokens": 150,
            "output_tokens": 300,
            "total_tokens": 450,
            "model_used": "gpt-35-turbo-16k-0613",
            "safety_checks_passed": true,
            "guardrails_triggered": []
        }
    ]
}

Retention: 90 days (configurable per customer)

6.3 Incident Response¶

If AI Causes Harm:

Immediate Actions (< 1 hour):
Disable affected chatbot
Notify affected users
Begin incident investigation
Investigation (< 24 hours):
Review conversation logs
Identify root cause
Assess scope of impact
Remediation (< 72 hours):
Fix underlying issue
Update guardrails
Retrain model if needed
Compensate affected users (if applicable)
Prevention (< 1 week):
Update safety procedures
Add new test cases
Conduct team training
Document lessons learned

7. Monitoring & Evaluation¶

7.1 AI Ethics Metrics¶

Metric	Target	Frequency	Owner
Safety violations	< 0.1% of interactions	Daily	ML Team
Bias audit score	> 90/100	Quarterly	Ethics Committee
User trust score	> 4.0/5	Monthly	Product Team
Transparency compliance	100%	Weekly	Legal
Consent rate	> 95% explicit consent	Monthly	Compliance

7.2 Continuous Monitoring¶

Real-Time Dashboards:

AI Ethics Dashboard:
├── Safety
│   ├── Blocked queries: 23 today
│   ├── False positives: 2 (reviewed)
│   └── Escalations: 1 pending
├── Fairness
│   ├── Response quality parity: 98%
│   └── Next bias audit: 45 days
├── Transparency
│   ├── Disclosure shown: 100%
│   └── Citations included: 87%
└── User Control
    ├── Consent rate: 97%
    └── Data deletion requests: 5 today

8. Compliance & Governance¶

8.1 Regulatory Alignment¶

India (DPDPA 2023):

✅ Explicit consent for data processing (Section 6)
✅ Right to access data (Section 13)
✅ Right to erasure (Section 15)
✅ Data breach notification (Section 8)

EU (GDPR):

✅ Lawful basis for processing (Article 6)
✅ Data minimization (Article 5)
✅ Right to explanation (Article 22)
✅ Data portability (Article 20)

EU AI Act (Proposed - Future):

✅ High-risk AI classification assessment
✅ Transparency obligations for general-purpose AI
✅ Risk management system

8.2 Ethics Committee¶

Composition:

CTO (Chair)
Legal Counsel
ML Engineering Lead
UX/Product Manager
External Ethics Advisor (planned Q2 2025)

Responsibilities:

Quarterly ethics reviews
AI incident investigations
Policy updates
Bias audit oversight

Meeting Schedule: Monthly

9. Future Commitments¶

Q1 2025:

Implement automated bias testing pipeline
Add real-time fairness monitoring
Expand guardrail coverage (100% of sensitive topics)

Q2 2025:

Hire external ethics advisor
Conduct independent AI audit
Publish annual AI transparency report

Q3 2025:

Open-source bias testing framework
AI safety certification (ISO/IEC 42001)
Multi-language ethics documentation

Last Updated: 2025-12-26
Version: 1.0
Review Cycle: Quarterly
Next Review: 2025-03-31

"With great AI power comes great responsibility."

AI Ethics Guidelines¶

Executive Summary¶

1. Responsible AI Principles¶

1.1 Core Principles¶

Principle 1: Safety¶

Principle 2: Fairness¶

Principle 3: Transparency¶

Principle 4: Accountability¶

Principle 5: Privacy¶

Principle 6: Beneficence¶

2. Bias Detection & Mitigation¶

2.1 Types of Bias We Address¶

2.2 Bias Testing Framework¶

2.3 Debiasing Techniques¶

3. Transparency & Explainability¶

3.1 User-Facing Transparency¶

3.2 RAG Citations (Explainability)¶

3.3 Model Information Disclosure¶

4. User Consent & Control¶

4.1 Explicit Consent Requirements¶

4.2 User Control Mechanisms¶

4.3 Consent Withdrawal¶

5. Safety Guardrails¶

5.1 Content Filtering Layers¶

5.2 Guardrail Implementation¶

5.3 Blocked Content Response¶

6. Accountability Framework¶

6.1 Human Oversight¶

6.2 Audit Trails¶

6.3 Incident Response¶

7. Monitoring & Evaluation¶

7.1 AI Ethics Metrics¶

7.2 Continuous Monitoring¶

8. Compliance & Governance¶

8.1 Regulatory Alignment¶

8.2 Ethics Committee¶

9. Future Commitments¶

Related Documentation¶