Bias reduction refers to the process of minimizing unfair preferences or prejudices that can affect decision-making and outcomes. This is particularly important in areas like machine learning, where algorithms can unintentionally learn and perpetuate biases present in the training data. By identifying and addressing these biases, we can create more equitable systems that treat all individuals fairly. To achieve bias reduction, various techniques can be employed, such as data preprocessing, which involves cleaning and balancing datasets, or algorithmic adjustments, which modify how models make predictions. These strategies help ensure that the results are more representative and just, ultimately leading to better and more inclusive outcomes for everyone involved.