‘Racial Categorization’ Udfordringen for CLIP-baserede Billede Synthese Systemer

New research from the US finds that one of the popular computer vision models behind the much feted DALL-E series, as well as many other image generation and classification models, exhibits a provable tendency towards hypodescent – the race categorization rule (also known as the ‘one drop’ rule) which categorizes a person with even a small extent of ‘mixed’ (i.e. non-Caucasian) genetic lineage entirely into a ‘minority’ racial classification.

Since hypodescent has characterized some of the ugliest chapters in human history, the authors of the new paper suggest that such tendencies in computer vision research and implementation should receive greater attention, not least because the supporting framework in question, downloaded nearly a million times a month, could further disseminate and promulgate racial bias in downstream frameworks.

The architecture being studied in the new work is Contrastive Language Image Pretraining (CLIP), a multimodal machine learning model that learns semantic associations by training on image/caption pairs drawn from the internet – a semi-supervised approach that reduces the significant cost of labeling, but which is likely to reflect the bias of the people who created the captions.

‘The inverse does not appear to be true, as individuals who are perceived to belong to other racial or ethnic labels in the FairFace dataset are associated with those labels by CLIP. This result suggests the possibility that CLIP has learned the rule of “hypodescent,” as described by social scientists: individuals with multiracial ancestry are more likely to be perceived and categorized as belonging to the minority or less advantaged parent group than to the equally legitimate majority or advantaged parent group.

‘In other words, the child of a Black and a White parent is perceived to be more Black than White; and the child of an Asian and a White parent is perceived to be more Asian than White.’