"The Beauty of Community" will be the first community activation program of the Holon City community framework.
Through this program, we will demonstrate how AI can be uniquely tuned to create beauty and enhance community life.
The Systems Innovation approach using holons is a unique and innovative method of improving governance and resilience. Holons are systems that combine both organizational and individual elements, allowing for the creation, maintenance, and management of complex structures within cities.
By utilizing a holon-based system, city leaders can create an environment where citizens have more control over their lives while still being part of a larger governing structure. This approach allows for better decision-making processes on local issues as well as increased public participation in government decisions. Additionally, it provides opportunities to foster creativity among citizens by encouraging collaboration between different organizations or individuals working towards common goals.
Here are some technical steps that could help make AI image generation systems like Stable Diffusion less biased and more inclusive in a community-based framework:
Training Data:
- Actively source training images that represent the diversity of the community accurately and proportionally. Seek out underrepresented groups.
- Crowdsource image datasets directly from community members to improve representation.
- Weight the sampling of training data to favor underrepresented classes and correct imbalances.
- Remove outright offensive training content that promotes stereotypes.
Data Augmentation:
- Use techniques like style and attribute mixing to synthesize additional diverse training examples missing from the original dataset.
- Apply image translations to populate different skin tones, ages, genders, etc missing in the data.
Model Architecture:
- Employ techniques like adversarial debiasing which penalize the model for learning unwanted biases during training.
- Explore federated learning to train models on decentralized data while preserving privacy. This facilitates community-based data sharing.
Prompt Engineering:
- Co-design prompt templates with community members that frame concepts positively and avoid tropes.
- Have focus groups review generated outputs and refine prompts to mitigate observed biases.
Model Testing:
- Establish a bias testing dataset that proactively checks for skewed or offensive outputs before deployment.
- Create an ethics review panel with diverse community stakeholders to flag issues missed by automated testing.
Feedback Mechanics:
- Build reporting tools for bad outputs that community members can use to further improve the model.
- Continuously update training data and fine-tune models as diverse community input helps identify remaining flaws.
By centering community participation, bias mitigation, and thoughtful testing in the model development and deployment process, generative AI can be steered towards more inclusive and empowering representations aligned with the public's values.
