Bias Reduction Techniques

Bias reduction techniques are methods used to improve the fairness and accuracy of machine learning models by minimizing unfair or unintended preferences toward certain groups or outcomes. These techniques involve adjusting data, algorithms, or training processes to ensure the model makes more equitable decisions. Examples include rebalancing datasets to ensure diverse representation, applying fairness constraints during training, or testing models for bias and correcting any unfair patterns. Overall, they help create models that are more objective and trustworthy across different populations, ensuring decisions are fair and unbiased.