The bottom line, says William Agnew, a postdoctoral fellow in AI ethics at Carnegie Mellon University and one of the coauthors, is that “anything you put online can [be] and probably has been scraped.”
The researchers found thousands of instances of validated identity documents—including images of credit cards, driver’s licenses, passports, and birth certificates—as well as over 800 validated job application documents (including résumés and cover letters), which were confirmed through LinkedIn and other web searches as being associated with real people. (In many more cases, the researchers did not have time to validate the documents or were unable to because of issues like image clarity.)
A number of the résumés disclosed sensitive information including disability status, the results of background checks, birth dates and birthplaces of dependents, and race. When résumés were linked to people with online presences, researchers also found contact information, government identifiers, sociodemographic information, face photographs, home addresses, and the contact information of other people (like references).
Examples of identity-related documents found in CommonPool’s small-scale data set show a credit card, a Social Security number, and a driver’s license. For each sample, the type of URL site is shown at the top, the image in the middle, and the caption in quotes below. All personal information has been replaced, and text has been paraphrased to avoid direct quotations. Images have been redacted to show the presence of faces without identifying the individuals.
COURTESY OF THE RESEARCHERS
When it was released in 2023, DataComp CommonPool, with its 12.8 billion data samples, was the largest existing data set of publicly available image-text pairs, which are often used to train generative text-to-image models. While its curators said that CommonPool was intended for academic research, its license does not prohibit commercial use as well.
CommonPool was created as a follow-up to the LAION-5B data set, which was used to train models including Stable Diffusion and Midjourney. It draws on the same data source: web scraping done by the nonprofit Common Crawl between 2014 and 2022.
While commercial models often do not disclose what data sets they are trained on, the shared data sources of DataComp CommonPool and LAION-5B mean that the data sets are similar, and that the same personally identifiable information likely appears in LAION-5B, as well as in other downstream models trained on CommonPool data. CommonPool researchers did not respond to emailed questions.
And since DataComp CommonPool has been downloaded more than 2 million times over the past two years, it is likely that “there [are]many downstream models that are all trained on this exact data set,” says Rachel Hong, a PhD student in computer science at the University of Washington and the paper’s lead author. Those would duplicate similar privacy risks.
Good intentions are not enough
“You can assume that any large-scale web-scraped data always contains content that shouldn’t be there,” says Abeba Birhane, a cognitive scientist and tech ethicist who leads Trinity College Dublin’s AI Accountability Lab—whether it’s personally identifiable information (PII), child sexual abuse imagery, or hate speech (which Birhane’s own research into LAION-5B has found).
