Attempts to define human beauty using artificial intelligence may reveal more about bias in data than universal standards, according to a new analysis from the University of Virginia’s School of Data Science. Using computer vision and statistical modeling, researchers evaluated whether facial features align with the “Golden Ratio,” a mathematical formula often cited as an objective measure of attractiveness. Instead, the analysis found that demographic variation, not mathematical proportion, was the strongest factor shaping model outputs. This challenges long-standing assumptions that beauty can be quantified.Attempts to define human beauty using artificial intelligence may reveal more about bias in data than universal standards, according to a new analysis from the University of Virginia’s School of Data Science. Using computer vision and statistical modeling, researchers evaluated whether facial features align with the “Golden Ratio,” a mathematical formula often cited as an objective measure of attractiveness. Instead, the analysis found that demographic variation, not mathematical proportion, was the strongest factor shaping model outputs. This challenges long-standing assumptions that beauty can be quantified.[#item_full_content]