A Mathematical Model of the Hidden Feedback Loop Effect in Machine Learning Systems

arXiv:2405.02726v1 Announce Type: new
Abstract: Widespread deployment of societal-scale machine learning systems necessitates a thorough understanding of the resulting long-term effects these systems have on their environment, including loss of trustworthiness, bias amplification, and violation of AI safety requirements. We introduce a repeated learning process to jointly describe several phenomena attributed to unintended hidden feedback loops, such as error amplification, induced concept drift, echo chambers and others. The process comprises the entire cycle of obtaining the data, training the predictive …

abstract arxiv bias cs.lg cs.sy deployment eess.sy effects environment feedback hidden learning systems long-term loop loss machine machine learning process requirements safety scale systems type understanding