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In a study evaluating the bias in OpenAI’s CLIP, a model that pairs text and images and operates behind the scenes in the popular DALL-E image generator, University of Michigan researchers found that CLIP performs poorly on images that portray low-income and non-Western lifestyles.

“During a time when AI tools are being deployed across the world, having everyone represented in these tools is critical. Yet, we see that a large fraction of the population is not reflected by these applications—not surprisingly, those from the lowest social incomes. This can quickly lead to even larger inequality gaps,” said Rada Mihalcea, the Janice M. Jenkins Collegiate Professor of Computer Science and Engineering, who initiated and advised the project.

AI models like CLIP act as foundation models, or models trained on a large amount of unlabeled data that can be adapted to many applications. When AI models are trained with data reflecting a one-sided view of the world, that bias can propagate into downstream applications and tools that rely on the AI.

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