# Generative diffusion models

Published articles for Generative diffusion models.

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## When AI art has no author: Study finds generated images often can't be traced to training data

DevFeed: [When AI art has no author: Study finds generated images often can't be traced to training data](<https://devfeed.tech/articles/when-ai-art-has-no-author-study-finds-generated-images-often-can-t-be-traced-to-training-data-37986.md>)

Original publisher: [Read original article](<https://news.mit.edu/2026/when-ai-art-has-no-author-generated-images-often-cant-be-traced-to-training-data-0818>)

Author: Rachel Gordon | MIT CSAIL

Published: 2026-08-18T16:35:00Z

Content type: news

Language: en

Sources: [MIT AI News](<https://devfeed.tech/sources/mit-ai-news.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Research](<https://devfeed.tech/topics/ai-research.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Computer Science and Artificial Intelligence Laboratory (CSAIL)](<https://devfeed.tech/topics/computer-science-and-artificial-intelligence-laboratory-csail.md>)

Tags: [ablation](<https://devfeed.tech/tags/ablation.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-and-copyright-law](<https://devfeed.tech/tags/ai-and-copyright-law.md>), [ai-generated-images](<https://devfeed.tech/tags/ai-generated-images.md>), [ai-governance](<https://devfeed.tech/tags/ai-governance.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [arts](<https://devfeed.tech/tags/arts.md>), [arts-technology-and-society](<https://devfeed.tech/tags/arts-technology-and-society.md>), [attribution-decay](<https://devfeed.tech/tags/attribution-decay.md>), [causal-inference](<https://devfeed.tech/tags/causal-inference.md>), [computer-science-and-artificial-intelligence-laboratory-csail](<https://devfeed.tech/tags/computer-science-and-artificial-intelligence-laboratory-csail.md>), [computer-science-and-technology](<https://devfeed.tech/tags/computer-science-and-technology.md>), [counterfactual-analysis](<https://devfeed.tech/tags/counterfactual-analysis.md>), [counterfactual-radius](<https://devfeed.tech/tags/counterfactual-radius.md>), [data](<https://devfeed.tech/tags/data.md>), [data-attribution](<https://devfeed.tech/tags/data-attribution.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [david-gifford](<https://devfeed.tech/tags/david-gifford.md>), [diffusion-ensembles](<https://devfeed.tech/tags/diffusion-ensembles.md>), [electrical-engineering-and-computer-science-eecs](<https://devfeed.tech/tags/electrical-engineering-and-computer-science-eecs.md>), [ethics](<https://devfeed.tech/tags/ethics.md>), [generative-ai-images](<https://devfeed.tech/tags/generative-ai-images.md>), [generative-diffusion-models](<https://devfeed.tech/tags/generative-diffusion-models.md>), [image-similarity-metrics](<https://devfeed.tech/tags/image-similarity-metrics.md>), [images](<https://devfeed.tech/tags/images.md>), [law](<https://devfeed.tech/tags/law.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [machine-unlearning](<https://devfeed.tech/tags/machine-unlearning.md>), [mit-csail](<https://devfeed.tech/tags/mit-csail.md>), [mit-schwarzman-college-of-computing](<https://devfeed.tech/tags/mit-schwarzman-college-of-computing.md>), [model](<https://devfeed.tech/tags/model.md>), [model-interpretability](<https://devfeed.tech/tags/model-interpretability.md>), [paper](<https://devfeed.tech/tags/paper.md>), [privacy-preserving-machine-learning](<https://devfeed.tech/tags/privacy-preserving-machine-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [school-of-engineering](<https://devfeed.tech/tags/school-of-engineering.md>), [science](<https://devfeed.tech/tags/science.md>), [technology-and-policy](<https://devfeed.tech/tags/technology-and-policy.md>), [technology-and-society](<https://devfeed.tech/tags/technology-and-society.md>), [training-data-attribution](<https://devfeed.tech/tags/training-data-attribution.md>), [training-data-influence](<https://devfeed.tech/tags/training-data-influence.md>), [zheng-dai](<https://devfeed.tech/tags/zheng-dai.md>)

### AI overview

MIT CSAIL researchers describe attribution decay, a phenomenon in which the influence of individual training examples on a generative model's outputs diminishes as datasets grow. Their method removes training examples and retrains models to test whether generated samples change.

### Source excerpt

A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.