New research from MIT CSAIL indicates that for AI image generators trained on vast datasets, attributing specific outputs to individual training data sources becomes increasingly difficult, a phenomenon termed 'attribution decay'. This implies that at scale, removing a single image or even an entire artist's work from the training set may not significantly alter the generated images. This finding has critical implications for copyright, licensing, and accountability in AI-generated art.
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