Description: Bias adjustment is a critical process in the field of machine learning and data preprocessing, aimed at correcting inherent distortions in predictive models. This bias can arise from various sources, such as data selection, feature representation, or even the model’s own decisions. The goal of bias adjustment is to ensure that the model is fair and equitable, preventing it from favoring certain groups or outcomes to the detriment of others. This is especially relevant in applications where automated decisions can significantly impact people’s lives, such as in hiring, credit granting, or law enforcement. To achieve effective bias adjustment, various techniques can be employed, such as collecting more representative data, modifying algorithms, or implementing fairness metrics. In summary, bias adjustment is essential for developing machine learning models that are not only accurate but also ethical and responsible, promoting trust in artificial intelligence and its adoption in society.
