Struggling with a biased machine learning model? You're not alone! Biases can sneak into the data or the algorithm itself, tilting results unfairly. But don't worry, there are ways to tackle this head-on. From scrutinizing your data sources to continuous testing and employing fairness metrics, there are steps you can take to ensure your model's integrity. Remember, it's about creating technology that's fair for everyone. Have you faced biases in your models, and how did you address them?
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