Achieving Unbiased Predictions in Algorithmic Machine Learning.

Traditional machine learning algorithms often prioritize predictive performance, such as minimizing Mean Squared Error or maximizing F-score, over achieving statistical unbiasedness. However, in critical applications like official statistics, unbiased predictions are paramount. This research delves into the specific conditions under which algorithmic machine learning can yield unbiased predictions or classifications for a given finite population, even without relying on ideal true data models. The study examines how training data can be effectively sampled from a population and how a trained prediction algorithm can be fine-tuned to ensure unbiased outcomes for that specific population. Furthermore, it addresses methods for unbiasedly assessing the performance of out-of-sample predictions or classifications. The core of this inference relies on the known probability design of the samples and training sets, offering a robust alternative to assumptions about underlying data distributions.