guidelines.md

Guidelines for Responsible AI Development

Best Practices for Data Collection

• Use high-quality data: Use data that is accurate, complete, and relevant to the task at hand.
• Avoid biased data: Avoid using data that is biased or contains errors.
• Use diverse data: Use data that is diverse and representative of the population or task at hand.

Best Practices for Model Evaluation

• Use multiple evaluation metrics: Use multiple evaluation metrics to evaluate the performance of the AI model.
• Use human evaluation: Use human evaluation to evaluate the performance of the AI model.
• Continuously monitor and improve: Continuously monitor and improve the performance of the AI model.

Best Practices for Model Deployment

• Use transparent models: Use transparent models that provide clear explanations of the AI system's decisions.
• Use explainable models: Use explainable models that provide clear explanations of the AI system's decisions.
• Continuously monitor and improve: Continuously monitor and improve the performance of the AI model.