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https://github.com/dxns-hub/ethicalai
An Ethical AI framework, laying the foundation for success in AI development
https://github.com/dxns-hub/ethicalai
accountability ai-ethics ai-framework bias-detection data-handling deployment ethical-ai ethical-standards evaluation fairness model-training privacy transparency
Last synced: 9 days ago
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An Ethical AI framework, laying the foundation for success in AI development
- Host: GitHub
- URL: https://github.com/dxns-hub/ethicalai
- Owner: dxns-hub
- License: mit
- Created: 2024-10-27T00:53:45.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-11-12T21:43:35.000Z (3 months ago)
- Last Synced: 2025-01-27T08:58:34.689Z (10 days ago)
- Topics: accountability, ai-ethics, ai-framework, bias-detection, data-handling, deployment, ethical-ai, ethical-standards, evaluation, fairness, model-training, privacy, transparency
- Language: Python
- Homepage:
- Size: 17.6 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Ethhical AI standard
## Our Mission
We are dedicated to providing access and opportunities for persons with disabilities in their work lives, fostering a sense of community and belonging.
## Our Values
- **Compassion**: We care deeply about the well-being of every individual.
- **Strength**: Inspired by the resilience of our community.
- **Nurturing**: Creating a supportive environment for growth and success.
- **Exploration**: Encouraging innovation and new possibilities.
- **Perspective**: Embrace mistakes with humor to grow and learn from them.
## Follow Us
Stay updated with our latest news and events.
## Table of Contents
- [Components](#components)
- [Quick Start](#quick-start)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
# Ethical AI Framework
## Ethical Principles
- **Fairness**: Ensure models do not discriminate against any group.
- **Transparency**: Provide clear explanations of model decisions.
- **Privacy**: Protect user data and ensure confidentiality.
- **Accountability**: Maintain responsibility for AI outcomes.
- **Perspective**: Embrace mistakes with humor to grow and learn from them.
## Components
- **Data Handling**: Tools for data preprocessing and bias detection.
- **Model Training**: Methods for training models with ethical considerations.
- **Evaluation**: Metrics and tools for evaluating model fairness and performance.
- **Deployment**: Guidelines and tools for deploying models ethically.
## Quick Start
### Installation
```bash
pip install ethical-ai-framework
```
## Usage
```python
from ethical_ai_framework import DataHandler, ModelTrainer, Evaluator, Deployer
# Data Handling
data_handler = DataHandler()
data = data_handler.load_data('data.csv')
clean_data = data_handler.preprocess(data)
# Model Training
trainer = ModelTrainer()
model = trainer.train(clean_data)
# Evaluation
evaluator = Evaluator()
fairness_metrics = evaluator.evaluate_fairness(model, clean_data)
# Deployment
deployer = Deployer()
deployer.deploy(model)
```
## Contributing
We welcome contributions! Please see our contributing guidelines for more details.
## License
This project is licensed under the MIT License - see the LICENSE file for details.