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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

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

pip install ethical-ai-framework

Usage

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.