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AI’s attribution problem gets worse as models scale

computerworld.com · 1h · first reportShare

Diffusion models are becoming sophisticated enough that they can reproduce an image even when they don’t have access to the original. In a series of ‘what if’ scenarios, researchers associated with MIT’s Computer Science & Artificial Intelligence Laboratory (CSAIL) swapped out different training datasets to test the impact on image outputs when original image data was completely removed. It turns out that, at sufficient scale, nothing changed. The researchers call the phenomenon “ attribution decay ”: The more data a diffusion model is trained on, and the larger it gets, the less individual inputs matter. “If you take away a piece of data and the output of the model doesn’t change, then that piece of data didn’t affect the output,” Zheng Dai, lead author on the work, explained in an MIT blog post . These findings could have significant ramifications when it comes to resolving growing concerns about intellectual property (IP) and copyright infringement. Models can recreate images even i