Your online data might currently contribute to the training of generative artificial intelligence (AI) systems, but there's a fresh approach to safeguarding your privacy.
OpenAI has unveiled a solution that empowers users to prevent its web crawler from utilizing their websites to train GPT models. This initiative forms part of an ongoing discussion about whether Large Language Models (LLMs), such as ChatGPT, should be permitted to ingest user data.
Rebecca Morris, a cybersecurity educator, highlighted the potential implications of LLMs trained on web-scraped data. In an email interview with Lifewire, she pointed out that these models have potentially absorbed an extensive range of user-generated content, spanning from social media posts and online forum discussions to older blog entries. This extensive knowledge raises concerning scenarios, including the accidental disclosure of private individual information when responding to malicious prompts.
AI Data Privacy
OpenAI has outlined that website operators can explicitly prevent the GPTBot crawler from accessing their site. By adding GPTBot to a website's robots.txt file, the web crawler can be effectively blocked.
According to Ashu Dubey, CEO of Gleen AI, as more websites adopt measures to prevent LLMs from scraping their content, user privacy will be better safeguarded. In an email statement, Dubey emphasized the importance of this approach in enhancing user data protection.
"LLMs and AI hold significant potential, but it's crucial to have established guidelines and ethical boundaries to ensure consumer protection, and this move is a beneficial step in that direction," he commented.
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Nevertheless, OpenAI's action does have its limitations. Ashu Dubey pointed out that because larger LLMs like GPT4 are open source, there's a lack of genuine control over how user data obtained through scraping is utilized. "This opens doors for malicious actors to exploit user data for fraudulent activities and criminal purposes, among other, less malevolent uses of consumer data," he elaborated.
Rebecca Morris underlined that blocking OpenAI's crawler won't erase the data the AI company has already collected from a website. She also emphasized that this action won't prevent web crawlers from other AI firms, as instructions in a robots.txt file are essentially suggestions and not strict mandates that crawlers must adhere to.
Drawbacks of AI Training
Employing web crawlers for AI model training introduces a host of potential challenges, as highlighted by Morris. For instance, an LLM could imitate a user's distinctive writing style, effectively creating a digital duplicate.
The implications could be dire for content creators, particularly if this is carried out without their consent. Users might engage with the digital clone, inadvertently sidelining the original content.
Chris Were, CEO of Verida, emphasized that LLMs can only access information already publicly available on the web. While this approach doesn't have a direct bearing on user privacy, it empowers individuals and companies to exercise more control over data they oversee. For instance, experts in specific fields may wish to prevent their content from being incorporated into AI training models to safeguard intellectual property or to shield a business dependent on the information within the website.
Even chatbots like ChatGPT have the potential to employ publicly accessible data for inaccurate user identification. This concern is already prevalent in academia, where LLMs can erroneously "hallucinate" citations and sources.
"This situation introduces a unique reputation risk because the outputs of LLMs are presented with a high level of certainty and are compelling in their appearance of authenticity. Since these outputs can be disseminated widely without being held accountable, they pose an unprecedented threat to an individual's reputation," commented Joseph Miller, co-founder of Quivr, a data verification platform, in an email.
He further elaborated, "These outputs reference actual authors with established reputations, yet now these authors are being associated with what appears to be a credible document, but is, in reality, a 'hallucination'."
Regrettably, for individuals posting online content, there's limited recourse to prevent its use by LLMs. Miller indicated that content hidden behind a login or captcha might offer some protection. However, he stressed that primarily centralized models will require legal regulation. Regardless of LLMs, Miller's advice remains consistent: refrain from sharing online information that you wouldn't want to be scraped, as it's highly likely to be.
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