Temporal quality degradation in AI models

As AI models continue to advance into many real-life applications, their ability to maintain reliable quality over time becomes increasingly important. The principal challenge in this task stems from the very nature of current machine learning models, dependent on the data as it was at the time of training. In this study, we present the first analysis of AI aging: the complex, multifaceted phenomenon of AI model quality degradation as more time passes since the last model training cycle. Using datasets from four different industries (healthcare operations, transportation, finance, and weather) and four standard machine learning models, we identify and describe the main temporal degradation patterns. We also demonstrate the principal differences between temporal model degradation and related concepts that have been explored previously, such as data concept drift and continuous learning. Finally, we indicate potential causes of temporal degradation, and suggest approaches to detecting aging and reducing its impact.

Automated summary

This study presents the first analysis of AI aging, highlighting the phenomenon of AI model quality degradation over time. The research identifies and describes the main temporal degradation patterns using datasets from various industries and machine learning models. The study also differentiates temporal model degradation from other related concepts. Potential causes of temporal degradation are addressed, and approaches to detect and reduce its impact are suggested.