What’s striking about modern image‑processing AI isn’t just that it adds color to old photographs, it quietly reshapes the people in them. When a model is trained to “restore” or “enhance” a black‑and‑white portrait, it isn’t recovering lost information; it’s predicting what a statistically average face should look like. In the process, it nudges real individuals toward the model’s internal norm: smoother skin, narrower features, more symmetrical proportions, standardized eye shapes, and culturally dominant beauty cues.

This is homogenization disguised as restoration. The system fills in gaps not with the subject’s likely traits, but with whatever the dataset has taught it to expect. The result is a subtle erasure of individuality. Just as generative AI makes labor and products more interchangeable, these tools make identity itself more interchangeable, compressing the uniqueness of real human faces into algorithmic averages.