For her safety, Doe has opted to receive alerts from the US Department of Justice Victim Notification System any time she may be a victim in a new criminal investigation. Although she has received countless alerts, she was shocked when the CCCP notified her that it had identified AI-generated CSAM on xAI that depicted her. This re-traumatized Doe, whose complaint alleged that messages were found on online forums “between offenders chatting about creating AI generated CSAM of Plaintiff and other similarly situated known, legacy, victims of CSAM.”
Now, Doe fears that xAI has not only made it easier to make more violative images of the most distressing time in her life, but also that xAI allegedly has stored the images that Grok generates and uses those outputs to further train Grok. Because of this, she believes that Grok has been trained on both the initial set of images that have haunted her for more than 20 years and the more recent AI-generated ones.
This is the first case to accuse xAI of training on CSAM, and the complaint does not go into great detail on that claim. Previously, Ars reported on a controversial dataset that was later scrubbed after researchers found CSAM in the training data, but there’s no indication xAI trained on that data. In a press release from lawyers representing Doe, it explained that Doe’s images were included in a CSAM Hash List maintained by NCMEC, and “that same material” allegedly “was part of the dataset xAI used to build Grok’s image and video generating capabilities.” The complaint similarly only alleged that “CSAM depicting Plaintiff with its longstanding well-known hash values has been used as a part of the dataset used by xAI.”
This source doesn’t establish what you think it establishes.
First, it’s a September 2021 infographic, updated in February 2024, not some contemporary study demonstrating that “AI in general” is racially prejudiced. More importantly, the subject here is health-care algorithms, and the article is specifically discussing algorithms that were deliberately designed to use race as a variable or that learned disparities from historical health-care data.
In fact, the source explicitly says that these systems can unintentionally increase existing racial biases through the explicit use of race in predicting outcomes and risk. That’s not evidence that AI possesses racial prejudice. It’s evidence that humans designed algorithms using race as a predictive variable, despite race being a poor proxy for genetic differences. The article even gives examples of medical algorithms where researchers subsequently removed race from the calculation.
That’s an extremely important distinction you’re completely glossing over.
If I build an algorithm that says “Black = higher risk” and the algorithm consequently produces a racial disparity, I’ve demonstrated that my algorithm contains a problematic racial assumption. I have not demonstrated that “AI is inherently racist.” Likewise, if an algorithm uses health-care spending as a proxy for how sick someone is, and that proxy reflects existing racial disparities in access to health care, the resulting bias comes from the data and the proxy, not some intrinsic racial prejudice possessed by the AI.
And your source actually undermines the broader claim you’re trying to make. It explicitly discusses AI being used to reduce racial disparities and cites research where algorithmic approaches improved outcomes or reduced unexplained disparities.
So yes, algorithmic bias in health care is a real and well-documented problem. Nobody is disputing that. What you’re doing is taking evidence that specific algorithms can encode or reproduce human biases and extrapolating it into “AI itself is racially prejudiced.”
That’s not what your source says, and it isn’t what the evidence demonstrates.