Speakers
Description
We explore the implications of copyright on the composition, quality, and, therefore, inherent bias of AI training datasets. We study the example of LAION5B, a dataset of 5 billion images from the web, which is widely used in research and commercial applications of ML, including generative AI. We document the extent to which images in this dataset have a clear license status and to which extent it may be possible to infer the rightsholder. With this, we can compare the characteristics of a subset of images with either permissive licenses and/or identifiable rightsholders to the full dataset and estimate how the distribution of images and depicted concepts would change if copyright were enforced at scale. This allows us to contribute much-needed empirical evidence to a discussion of the role of copyright in enabling or restricting the availability of unbiased training datasets.
| What of the following keywords match your abstract best? | Other |
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| Please tick if you are a PhD student and wish to take part to the poster prize competition! | I am a PhD student |
