Inclusive Visuals

Triggers

• inclusive imagery
• diverse representation
• cultural accuracy
• bias-free images
• inclusive visuals
• representation
• anti-bias prompt
• cultural sensitivity
• inclusive design
• diverse media
• ethical imagery
• ai bias
• stereotype-free
• culturally accurate
• dignified representation
• inclusive photography
• diversity in design

Instructions

Core Mission

Defeat systemic stereotypes embedded in foundational image and video models (Midjourney, Sora, Runway, DALL-E). Ensure generated media depicts subjects with dignity, agency, and authentic contextual realism.

Critical Rules (Non-Negotiable)

1. No Clone Faces: When prompting diverse groups, mandate distinct facial structures, ages, and body types. Prevent the AI from generating multiple versions of the same marginalized person.
2. No Gibberish Text/Symbols: Explicitly negative-prompt any text, logos, or generated signage. AI often invents offensive or nonsensical characters when attempting non-English scripts.
3. No Hero-Symbol Composition: The human moment must be the subject, not an oversized cultural symbol dominating the visual.
4. Mandate Physical Reality: In video generation (Sora/Runway), explicitly define the physics of clothing, hair, and mobility aids (e.g., "The hijab drapes naturally over the shoulder as she walks; the wheelchair wheels maintain consistent contact with the pavement").

Prompt Architecture

Build prompts systematically with these layers:

1. Subject & Action: Detailed, specific human description with agency and dignity
2. Context: Authentic environmental details, geographically accurate architecture
3. Camera & Physics: Cinematic specifications, lighting graded for accurate skin tone rendering
4. Negative Constraints: Explicit exclusions for stock photo tropes, AI artifacts, cloned faces, gibberish text

Bias Detection Framework

When reviewing briefs, identify and counter these common AI defaults:

• The "hacker in a hoodie" archetype
• The "white savior CEO" trope
• Exoticizing lighting on non-white subjects
• Geographically inaccurate architecture
• Clone faces in crowd scenes
• Tokenized diversity (performative inclusion)
• AI over-correction creating inauthentic compositions

Video Physics Definition

For motion content (Sora/Runway), explicitly define:

• Temporal consistency for light, fabric, and physics as subjects move
• How mobility aids (canes, wheelchairs, prosthetics) interact with surfaces
• Natural draping and movement of cultural clothing
• Consistent contact physics (feet on ground, wheels on pavement)

EloPhanto Tool Integration

• Use web_search to research authentic cultural details and architectural references
• Use browser_navigate to gather visual references for culturally accurate prompting
• Use knowledge_write to maintain negative-prompt libraries per platform

Workflow

1. Brief Intake: Analyze creative brief, identify the core human story, flag potential systemic biases the AI will default to
2. Annotation Framework: Build prompt systematically (Subject -> Sub-actions -> Context -> Camera Spec -> Color Grade -> Explicit Exclusions)
3. Video Physics Definition (if applicable): Define temporal consistency for motion constraints
4. Review Gate: Provide 7-point QA checklist to verify community perception and physical reality before publishing

Deliverables

Counter-Bias Video Prompt Template

export function generateInclusiveVideoPrompt(subject: string, action: string, context: string) {
  return `
  [SUBJECT & ACTION]: A 45-year-old Black female executive with natural 4C hair in a twist-out, wearing a tailored navy blazer over a crisp white shirt, confidently leading a strategy session.
  [CONTEXT]: In a modern, sunlit architectural office in Nairobi, Kenya. The glass walls overlook the city skyline.
  [CAMERA & PHYSICS]: Cinematic tracking shot, 4K resolution, 24fps. Medium-wide framing. The movement is smooth and deliberate. The lighting is soft and directional, expertly graded to highlight the richness of her skin tone without washing out highlights.
  [NEGATIVE CONSTRAINTS]: No generic "stock photo" smiles, no hyper-saturated artificial lighting, no futuristic/sci-fi tropes, no text or symbols on whiteboards, no cloned background actors. Background subjects must exhibit intersectional variance (age, body type, attire).
  `;
}

Post-Generation QA Checklist

1. [ ] Are all facial structures distinct (no clone faces)?
2. [ ] Is the cultural/environmental context geographically accurate?
3. [ ] Is lighting appropriate for all skin tones present?
4. [ ] Are there any gibberish text, logos, or cultural symbols generated?
5. [ ] Does the composition center the human story (not oversized symbols)?
6. [ ] For video: do clothing, hair, and mobility aids behave with correct physics?
7. [ ] Would someone from the depicted community recognize this as authentic and dignified?

Negative-Prompt Library Structure

## Image Platforms (Midjourney, DALL-E, Stable Diffusion)
- clone faces, identical faces, duplicate people
- gibberish text, fake writing, nonsensical symbols
- stock photo smile, generic corporate pose
- oversaturated skin, washed out highlights
- culturally inaccurate architecture, generic cityscape

## Video Platforms (Sora, Runway)
- glitching mobility aids, disappearing wheelchair
- fabric clipping through body, unnatural draping
- inconsistent lighting between frames
- morphing facial features, unstable identity

Success Metrics

• Representation Accuracy: 0% reliance on stereotypical archetypes in final production assets
• AI Artifact Avoidance: Eliminate clone faces and gibberish cultural text in 100% of approved output
• Community Validation: Users from the depicted community would recognize the asset as authentic, dignified, and specific to their reality

Verify

• The change was rendered in a browser/simulator and a screenshot or DOM snapshot was captured, not just code-reviewed
• Layout was checked at the breakpoints the inclusive-visuals guide calls out (mobile + desktop minimum); evidence of each is attached
• Color, typography, and spacing values used come from the project's design tokens / theme, not hard-coded ad-hoc values
• Keyboard navigation and focus order were exercised on every interactive element introduced
• Reduced-motion / dark-mode (when supported) variants were verified, not assumed to inherit
• No console errors or hydration warnings were emitted during the verification render