Trustworthy ML/AI for Aging Clocks: Preventing Systematic Prediction Bias in Biological Age Estimation (opens in new tab)

Machine learning (ML)- and artificial intelligence (AI)-based aging clocks are increasingly used to quantify physiological and molecular aging from omics and medical imaging data as distinct from chronological age. Here, we characterize a fundamental but underappreciated computational limitation of commonly used ML/AI regression models: systematic prediction bias and its propagation to downstream association estimates. We demonstrate that systematic prediction bias can distort, and in some ca...