Ensuring equity in model development requires prioritizing the identification of dependable data sources for predictive modeling. This section highlights the significance of understanding the context of data collection, ensuring dataset diversity, and addressing biases to achieve equitable model performance. Additionally, the section touches on ethical considerations in data collection and emphasizes the need for comprehensive, fair, and ethically sound methodologies in developing machine learning models.

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Developers wishing to dive deeper into the technical aspects of ensuring equity in AI can access our GitHub site.