Embryo selection tools in IVF can favour XY embryos: implications for equitable reproductive AI

Assisted reproductive technologies (ART) are increasingly used worldwide, with up to 8.5% of babies in some countries conceived via in vitro fertilisation (IVF). Embryo selection, a critical step in IVF, is shifting from manual morphology-based to artificial intelligence (AI)-based assessment. While these systems use outcome data to select embryos with the highest likelihood of pregnancy, their potential to introduce unintended bias has not been evaluated at scale. We analysed 1,334 human embryos with known sex (from preimplantation genetic testing), graded by two widely-used AI models (KIDScore D3™, CHLOE EQ™) and standard manual morphology-based Gardner grading. Manual grading and the traditional AI model (KIDScore D3™) assigned higher scores to XY embryos; the deep-learning model (CHLOE EQ™) did not. Simulations suggest such biases could modestly skew sex ratios at birth in high-IVF-utilisation settings, which is of global concern. These findings highlight the need for systematic bias evaluation in embryo grading and demonstrate that algorithmic selection without sex bias is achievable with appropriate design.