# Data Fabrication Prohibition

## 🚨 CRITICAL RULE: NO DATA FABRICATION ALLOWED 🚨

**ALL DATA MUST BE REAL AND VERIFIABLE.** Fabricating any data - even as placeholders - is strictly prohibited and violates project integrity.

## What Constitutes Data Fabrication

### ❌ FORBIDDEN (Never do this):
- Creating fake names for people
- Inventing job titles or companies
- Making up education history
- Generating placeholder skills or experience
- Creating fictional LinkedIn URLs
- Any "fallback" data when real data is unavailable

### ✅ ALLOWED (What you CAN do):
- Skip profiles that cannot be extracted
- Return `null` or empty fields for missing data
- Mark profiles with `extraction_error: true`
- Log why extraction failed
- Use "Unknown" or "Not specified" for display purposes (not in stored data)

## Real-World Example of Violation

**What happened**: The script created a fake profile for "Celyna Keates" when the Exa API failed.

**Why this is wrong**:
- The person does not exist
- The LinkedIn URL was fabricated
- All profile data was invented by the LLM
- This pollutes the dataset with false information

**Correct approach**:
- When API fails, skip the profile
- Log the failure
- Do NOT create any fallback data

## Technical Implementation

### In Extraction Scripts

```python
# ❌ WRONG - Creating fallback data
if not profile_data:
    profile_data = {
        "name": "Unknown Person",
        "headline": "No data available",
        "experience": []
    }

# ✅ CORRECT - Skip when no real data
if not profile_data or profile_data.get("extraction_error"):
    print(f"Skipping {url} - extraction failed")
    return None  # Do NOT save anything
```

### In Validation Functions

```python
def validate_profile(profile_data):
    # Must have real name from LinkedIn
    if not profile_data.get("name") or len(profile_data["name"]) < 2:
        return False
    
    # Name must not be generic/fabricated
    name = profile_data["name"].lower()
    generic_names = ["linkedin member", "unknown", "n/a", "not specified"]
    if name in generic_names:
        return False
    
    return True
```

## User Requirements

From project leadership:
- **"ALL PROFILES SHOULD BE REAL!!!"**
- **"Fabricating data is strictly prohibited"**
- **"Better to have missing data than fake data"**

## Consequences of Violation

1. **Data Integrity**: Fabricated data corrupts research value
2. **Trust**: Undermines confidence in entire dataset
3. **Legal**: May constitute misrepresentation
4. **Reputation**: Damages project credibility

## When in Doubt

1. **Don't save** if you cannot verify data authenticity
2. **Log the issue** for manual review
3. **Skip the record** - it's better to have no data than bad data
4. **Ask for clarification** if unsure about validation rules

## Reporting Violations

If you discover fabricated data:
1. Immediately remove the fabricated records
2. Document how it happened
3. Implement safeguards to prevent recurrence
4. Report to project maintainers

Remember: **Real data or no data.** There is no third option.