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AI BEST PRACTICES

Data Collection and Preparation

Ensure data is relevant and high quality

Address bias and fairness in data

Implement data governance and security measures

Model Development and Evaluation

Select appropriate algorithms for the task

Regularly evaluate model performance and update as needed

Test for robustness and reliability

Deployment and Monitoring

Implement proper version control for models

Monitor model performance in production

Have a plan for model updates and retraining

Ethical Considerations

Ensure transparency in model decision-making

Address potential ethical concerns in AI applications

Have a process for handling bias and discrimination issues

Regulatory Compliance

Understand and comply with relevant data protection regulations

Ensure data privacy and security measures are in place

Keep track of changing regulatory landscape and adjust practices accordingly.

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