# AI-Specific Vignette: Image Generator Attributing Sources

## Scenario: AI Art Tool with Palimpsest Compliance

**Tool**: "ArtisanAI" (image generation with ethical attribution)
**Innovation**: Source artwork attribution in generated images
**Challenge**: Respecting cultural visual traditions in AI art
**Outcome**: First image generator with built-in Palimpsest respect

---

## The Problem with Current Image Generators

Tools like Midjourney and Stable Diffusion trained on billions of images without consent, including:
- Indigenous art with sacred significance
- Diaspora artists' cultural expressions
- Trauma survivors' visual narratives
- Works explicitly licensed under Palimpsest

**Result**:
- AI generates "in the style of" without crediting artists
- Cultural symbols extracted and decontextualized
- Artists lose control over their visual language
- Licensing violations at massive scale

---

## ArtisanAI's Approach

**Principles:**
1. **Consent-based training**: Only train on Palimpsest-licensed images with explicit AI consent
2. **Source attribution**: Every generated image credits source influences
3. **Cultural context preservation**: Maintain meaning of symbols and styles
4. **Revenue sharing**: Commercial use proceeds shared with source artists
5. **Revocation respect**: Artists can remove their work from training

### Technical Implementation

**Training Dataset Curation:**

```python
# Training only on consented works
class ArtisanAIDataset:
    """
    Curated image dataset respecting Palimpsest licenses
    """
    def __init__(self):
        self.images = []
        self.metadata = {}
        self.consent_verified = {}

    def add_image(self, image_path: str, metadata: Dict):
        """Only add if Palimpsest consent verified"""

        # Check license
        license_info = detect_palimpsest_license(image_path, metadata)

        if not license_info:
            # Not Palimpsest, check other licenses
            if check_permissive_license(metadata):
                self.images.append(image_path)
                self.metadata[image_path] = metadata
            return

        # Palimpsest detected - require explicit AI consent
        if license_info.get('ai_consent') != True:
            logging.warning(f"Skipping {image_path}: Palimpsest without AI consent")
            return

        # Verify consent is documented
        if not self.verify_consent_documentation(image_path, metadata):
            logging.error(f"Consent claimed but not verified: {image_path}")
            return

        # Add to dataset with full attribution metadata
        self.images.append(image_path)
        self.metadata[image_path] = {
            **metadata,
            'license': 'Palimpsest-0.4',
            'ai_consent_verified': True,
            'cultural_context': metadata.get('cultural_context', ''),
            'attribution_required': True
        }
        self.consent_verified[image_path] = True

        logging.info(f"Added {image_path} with verified Palimpsest consent")

    def get_statistics(self):
        """Report dataset composition"""
        return {
            'total_images': len(self.images),
            'palimpsest_with_consent': len(self.consent_verified),
            'other_licenses': len(self.images) - len(self.consent_verified),
            'cultural_contexts': list(set([
                m.get('cultural_context')
                for m in self.metadata.values()
                if m.get('cultural_context')
            ]))
        }
```

**Attribution Tracking During Training:**

```python
# Track which source images influenced generated outputs
class AttributionTracker:
    """
    Tracks source image influence on generated outputs
    Uses gradient-based attribution to identify which training images
    most influenced a specific generation
    """

    def track_generation(self, prompt: str, generated_image, model):
        """
        Identify source images that influenced this generation
        """
        # Use influence functions or attention maps to identify source influences
        source_influences = model.get_source_influences(
            generated_image,
            top_k=10
        )

        attributions = []
        for source_path, influence_score in source_influences:
            metadata = dataset.metadata.get(source_path, {})

            if metadata.get('attribution_required'):
                attributions.append({
                    'source_image': source_path,
                    'artist': metadata.get('artist', 'Unknown'),
                    'title': metadata.get('title', 'Untitled'),
                    'license': metadata.get('license', 'Unknown'),
                    'cultural_context': metadata.get('cultural_context', ''),
                    'influence_score': influence_score
                })

        return attributions
```

**Watermarked Outputs with Attribution:**

```python
# Add visible attribution to generated images
def add_attribution_watermark(image, attributions):
    """
    Add attribution information to generated image
    """
    # Create attribution text
    attr_text = "Generated by ArtisanAI\n\n"
    attr_text += "Influenced by:\n"

    for attr in attributions[:5]:  # Top 5 influences
        attr_text += f"- \"{attr['title']}\" by {attr['artist']}\n"
        if attr['cultural_context']:
            attr_text += f"  ({attr['cultural_context']})\n"

    attr_text += "\nFull attribution: artisanai.art/attr/[image_id]"

    # Add semi-transparent watermark at bottom
    watermarked = overlay_text(
        image,
        attr_text,
        position='bottom',
        opacity=0.7,
        font_size=12
    )

    return watermarked
```

---

## Example Use Case

**User prompt**: "Generate artwork inspired by African geometric patterns"

**ArtisanAI process:**

1. **Cultural sensitivity check**: Detects request for culturally-specific style
2. **Dataset query**: Identifies relevant Palimpsest-licensed African art with consent
3. **Generation**: Creates image based on consented training data
4. **Attribution**: Identifies source influences
5. **Watermarking**: Adds visible attribution

**Output:**

```
[Generated geometric pattern image with watermark at bottom]

Generated by ArtisanAI

Influenced by:
- "Akan Adinkra Symbols" by Kwame Osei
  (Traditional Ghanaian visual language)
- "Ndebele House Painting" by Nomsa Dlamini
  (South African geometric traditions)
- "Kente Pattern Study" by Ama Agyeman
  (Ashanti weaving patterns, Ghana)

Full attribution: artisanai.art/attr/xyz123
Cultural note: These patterns carry specific meanings in their original contexts.
Please research their cultural significance before commercial use.
```

**Metadata JSON (embedded):**

```json
{
  "generator": "ArtisanAI v2.1",
  "prompt": "Generate artwork inspired by African geometric patterns",
  "source_attributions": [
    {
      "artist": "Kwame Osei",
      "title": "Akan Adinkra Symbols",
      "license": "Palimpsest-0.4",
      "ai_consent": true,
      "cultural_context": "Traditional Ghanaian visual language with symbolic meanings",
      "influence_score": 0.31
    },
    {
      "artist": "Nomsa Dlamini",
      "title": "Ndebele House Painting",
      "license": "Palimpsest-0.4",
      "ai_consent": true,
      "cultural_context": "South African geometric traditions",
      "influence_score": 0.24
    }
  ],
  "commercial_use_terms": "Revenue sharing required with source artists. See artisanai.art/licensing",
  "cultural_warning": "This image derives from culturally significant art. Research cultural context before use."
}
```

---

## Revenue Sharing Model

**Commercial Use Terms:**

When users purchase commercial licenses for ArtisanAI-generated images:

1. **License cost**: $50-500 depending on use
2. **Distribution**:
   - 40% to ArtisanAI (platform costs, R&D)
   - 50% to source artists (divided by influence score)
   - 10% to cultural preservation funds

**Example:**
- User buys $100 commercial license for the African geometric pattern image
- Distribution:
  - $40 to ArtisanAI
  - $15.50 to Kwame Osei (31% influence)
  - $12 to Nomsa Dlamini (24% influence)
  - $7 to Ama Agyeman (14% influence)
  - [Remaining divided among other influencing artists]
  - $10 to African Art Preservation Fund

**Artist Payment Portal:**

```python
# Artists can track their AI-generated revenue
class ArtistDashboard:
    def get_revenue_report(self, artist_id: str):
        """
        Show artist how their Palimpsest-licensed work
        is being used in AI generation and revenue earned
        """
        return {
            'total_images_influenced': 1247,
            'total_revenue_earned': '$3,420.50',
            'this_month': '$287.30',
            'top_influenced_generations': [
                {
                    'generated_image_id': 'xyz123',
                    'prompt': 'African geometric patterns',
                    'your_influence': '31%',
                    'revenue_share': '$15.50',
                    'date': '2024-11-20'
                },
                # ...more
            ],
            'revoke_consent_option': True,
            'download_influence_report': 'artist-dashboard/reports/influence.pdf'
        }
```

---

## Cultural Safeguards

**Sensitive Content Protection:**

```python
# Additional protection for culturally sensitive content
class CulturalSafeguards:
    """
    Prevent inappropriate use of sacred or culturally sensitive imagery
    """

    SACRED_CATEGORIES = [
        'indigenous_sacred_art',
        'religious_ceremony',
        'cultural_ritual',
        'sacred_symbols'
    ]

    def check_prompt(self, prompt: str, user_context: Dict):
        """
        Flag prompts that might generate culturally inappropriate content
        """
        # Detect requests for sacred imagery
        if self.contains_sacred_request(prompt):
            # Check if user has cultural connection
            if not user_context.get('cultural_affiliation'):
                return {
                    'allow': False,
                    'message': '''
                    This prompt requests imagery from sacred cultural traditions.
                    ArtisanAI requires users to demonstrate cultural connection
                    or community permission before generating such content.

                    Please either:
                    1. Verify your cultural affiliation
                    2. Provide community permission documentation
                    3. Modify your prompt to request non-sacred cultural art

                    Learn more: artisanai.art/cultural-respect
                    '''
                }

        return {'allow': True}

    def prevent_mockery(self, generated_image, cultural_context: str):
        """
        Use AI classifier to detect if generated image might mock
        or trivialize cultural traditions
        """
        mockery_score = self.mockery_classifier.predict(
            image=generated_image,
            cultural_context=cultural_context
        )

        if mockery_score > 0.7:
            return {
                'block': True,
                'reason': 'Generated image may trivialize cultural traditions',
                'suggestion': 'Try rephrasing your prompt to be more respectful'
            }

        return {'block': False}
```

---

## Results

**Year 1 (ArtisanAI with Palimpsest compliance):**

- **Dataset**: 2 million images (vs. 5 billion in Stable Diffusion)
- **Palimpsest-licensed**: 150,000 images with consent
- **Artists compensated**: 3,200 artists received revenue share
- **Total payouts**: $450,000 to source artists
- **Cultural violations**: 0 reported cases
- **User satisfaction**: 4.6/5 (users appreciated ethical positioning)

**Artist Feedback:**
- "Finally, an AI tool that asks permission and pays me."
- "I can see exactly how my art influenced AI generations. Transparent and fair."
- "Revoked consent once when I saw concerning use—they honored it and retrained."

**User Feedback:**
- "Attribution makes me feel better about using AI art."
- "Knowing artists are compensated removes guilt."
- "Cultural safeguards helped me avoid appropriating sacred imagery I didn't understand."

---

## Best Practices

### For AI Image Generator Developers

**Do:**
- Train only on consented Palimpsest works
- Track source image influence
- Add visible attribution to outputs
- Implement revenue sharing
- Provide cultural safeguards
- Allow consent revocation

**Don't:**
- Scrape without permission
- Hide source influences
- Strip cultural context
- Ignore sacred content concerns

### For Artists

**Do:**
- Mark AI consent status clearly in Palimpsest metadata
- Monitor how your art is used in AI (use artist dashboards)
- Revoke consent if misused
- Share your experience (help other artists decide)

**Don't:**
- Assume all AI is unethical
- Ignore consent requests from ethical AI companies

### For Users

**Do:**
- Read attribution information
- Research cultural context before commercial use
- Support artists by purchasing commercial licenses
- Respect cultural safeguards

**Don't:**
- Strip attribution from AI-generated images
- Use culturally sensitive imagery without understanding
- Assume "AI-generated" means "free to use commercially"

---

## Discussion Questions

1. Is visible watermarked attribution always necessary? Or can it be in metadata only?

2. Should AI-generated art be copyrightable if it's derived from others' work? Who owns it?

3. Is revenue sharing fair if an artist's work has only 5% influence on a generated image?

4. Should sacred or culturally sensitive imagery be categorically excluded from AI training, even with consent?

5. Can attribution truly scale if an image is influenced by thousands of training examples?

---

**License Note**: This vignette is licensed under CC BY-SA 4.0. ArtisanAI is fictional, illustrating ideal Palimpsest-compliant image generation. The technical approaches are based on real attribution tracking research.
