The Data Provenance Initiative analyzed data sets used to build generative AI and found confusion surrounding licensing and fair use.
Outside the leading artificial intelligence laboratories, most new-product developers don’t start from scratch. They begin with an off-the-shelf AIMeta’s open-source language model — then turn to online repositories such as GitHub and Hugging Face for data sets that can teach generativethe specialized data used to teach AI models to excel at a particular task, a process called “fine-tuning.
Shayne Longpre, a PhD candidate at the MIT Media Lab who researches large language models and led the audit, said that hosting sites allow users toThe lack of proper documentation is a community-wide problem that stems from modern machine-learning practices, Longpre said. Data archives are often combined, repackaged and re-licensed numerous times.
AI companies have grown increasingly secretive about the data they use to train and refine popular AI models. The goal of the new research is to offer engineers, policymakers and lawyers visibility into the murky ecosystem of data fueling the generative AI gold rush. As part of the analysis, researchers also tracked patterns across data sets, including the years that the data was collected and the geographic location of data set creators.
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