MID-Space: Aligning Diverse Communities’ Needs to Inclusive Public Spaces

2024-12-15

Author: Rashid Mushkani

MID-Space: Aligning Diverse Communities’ Needs to Inclusive Public Spaces Thumbnail

Example visualizations generated using Stable Diffusion XL, fine-tuned with the MID-Space dataset.

Project Team: Shravan Nayak, Rashid Mushkani, Hugo Berard, Allison Cohen, Shin Koseki, Hadrien Bertrand, Emmanuel Beaudry Marchand, Toumadher Ammar, Jerome Solis.

Project Overview

The MID-Space dataset bridges the gap between AI-generated visualizations and diverse community preferences in public space design. Created through participatory workshops and fine-tuned using Stable Diffusion XL, the dataset aligns AI outputs with six key criteria:

  • Accessibility
  • Safety
  • Diversity
  • Inclusivity
  • Invitingness
  • Comfort

This initiative empowers marginalized communities to actively shape urban design, promoting inclusive, equitable, and user-centered public spaces.

Dataset Features

  • Textual Prompts: 3,350 prompts representing diverse public space typologies.
  • AI-Generated Images: 13,465 visualizations created with Stable Diffusion XL.
  • Annotations: Over 42,000 (in raw) and 35,000 distinct annotations evaluating preferences on a -1 to +1 scale for up to three criteria per image pair.

Data Collection Process

  1. Community Workshops:

    • Conducted three workshops with diverse Montreal communities to identify six alignment criteria.
    • Generated 440 textual prompts, expanded to 2,910 synthetic prompts using GPT-4.
  2. Image Generation:

    • Stable Diffusion XL created 20 images per prompt, refined using CLIP similarity scoring.
  3. Human Annotation:

    • Sixteen annotators evaluated image pairs through an accessible web interface.

Visual Documentation

Fine-tuned visualization emphasizing accessibility. Fine-tuned visualization emphasizing safety.

Visualization promoting diversity and inclusivity. Visualization promoting inclusivity versus all criteria.

Applications

The MID-Space dataset is a valuable resource for:

  • AI Alignment Research: Developing models that better reflect pluralistic human values.
  • Urban Design: Crowd-sourcing input for inclusive public space design.
  • Generative AI Tools: Enhancing text-to-image models for equity-focused visualization tasks.

Further explore by clicking here: MID-Space Viewer

Related Links

Tags

Generative AI Urban Planning Inclusivity Community Engagement Public Spaces

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