{"id":27365,"date":"2024-06-24T01:47:56","date_gmt":"2024-06-24T08:47:56","guid":{"rendered":"https:\/\/www.privateinternetaccess.com\/blog\/?p=27365"},"modified":"2024-06-24T01:47:58","modified_gmt":"2024-06-24T08:47:58","slug":"get-ready-for-generative-ais-next-assault-on-your-privacy","status":"publish","type":"post","link":"https:\/\/www.privateinternetaccess.com\/blog\/get-ready-for-generative-ais-next-assault-on-your-privacy\/","title":{"rendered":"Get Ready for Generative AI\u2019s Next Assault On Your Privacy"},"content":{"rendered":"\n

In the 15 months since we first warned that generative AI would be terrible for privacy<\/a>, the potential threats have become clearer. One example of the new problems generative AI brings is the increasing popularity of romantic chatbots<\/a>, which can explicitly request \u2013 and have often obtained \u2013 highly intimate data. There\u2019s a new trend emerging that could be even more problematic than what we\u2019ve seen so far.<\/p>\n\n\n\n

It arises from generative AI\u2019s apparently insatiable demand for training data. The more high-quality data a system is trained on, the more likely it is to deliver good output. In efforts to position themselves as market leaders, companies are looking for new sources of decent material to use to train the LLMs (large language models) that generative AI products are based on.\u00a0<\/p>\n\n\n\n

How LLMs Are Trained<\/strong><\/h2>\n\n\n\n

An article in the New York Times explains that until now, primary AI training sets have been sourced from just a few places. One is web pages collected since 2007; another is Wikipedia; two more are thought to be based on text from millions of published books. Another major dataset consists of popular web pages that have been linked from Reddit. Initially created as proprietary training data by OpenAI, there\u2019s also a completely free open-source version<\/a> that anyone can download.<\/p>\n\n\n\n

The big companies in the generative AI space are now looking for untapped sources of high-quality material to train their LLMs. For example, the New York Times reported that Meta discussed buying<\/a> an entire publishing house, effectively acquiring a large number of texts for future LLM training. Another approach that\u2019s being considered is paying people directly to train generative AI systems, either by interacting with an AI chatbot to improve its quality, or simply writing new material for it.\u00a0\u00a0<\/p>\n\n\n\n

The New Threat to Privacy: User-Generated Material<\/strong><\/h2>\n\n\n\n

There\u2019s a more important issue at play, though. Last year, Zoom updated its terms and conditions in a way that seemed to grant the company the right to train AI systems on material generated by its users<\/a>. After public outcry, the company quickly \u201cclarified\u201d matters by assuring customers that it would not use audio, video, or chat data to train its artificial intelligence models without customers\u2019 permission. In the future, the company might make these permissions an essential condition of use.\u00a0<\/p>\n\n\n\n

We\u2019re starting to see this approach from other companies, too. For example, just a month after Zoom\u2019s AI blunder, Meta announced its generative AI features<\/a>, explaining that:<\/p>\n\n\n\n

\n

Generative AI models take a large amount of data to effectively train, so a combination of sources are used for training, including information that\u2019s publicly available online, licensed data and information from Meta\u2019s products and services. For publicly available online information, we filtered the dataset to exclude certain websites that commonly share personal information. Publicly shared posts from Instagram and Facebook \u2013 including photos and text \u2013 were part of the data used to train the generative AI models underlying the features we announced at Connect. We didn\u2019t train these models using people\u2019s private posts. We also do not use the content of your private messages with friends and family to train our AIs.\u00a0<\/em><\/p>\n<\/blockquote>\n\n\n\n

In other words, private posts are excluded from the training set, but public ones aren\u2019t \u2013 even though they might contain a great deal of highly personal data.\u00a0However, following a request from the Irish Data Protection Commission, Meta has announced a \u201cdelay\u201d in training its LLMs using public content shared by adults on Facebook and Instagram in Europe.<\/p>\n\n\n\n

Google also aims to use publicly available information from its users in the same way. In July 2023, it updated its privacy policy<\/a> to allow the use of user data \u201cto help train Google\u2019s AI models and build products and features like Google Translate, Bard, and Cloud AI capabilities.\u201d Google has also been busy signing deals with other services to add new user-generated material to its training sets. For example, in February, Google announced an expanded partnership with Reddit<\/a>:<\/p>\n\n\n\n

Google now has access to Reddit\u2019s Data API, which delivers real-time, structured, unique content from their large and dynamic platform. With the Reddit Data API, Google will now have efficient and structured access to fresher information, as well as enhanced signals that will help us better understand Reddit content and display, train on, and otherwise use it in the most accurate and relevant ways. This expanded partnership does not change Google\u2019s use of publicly available, crawlable content for indexing, training, or display in Google products.<\/em><\/p>\n\n\n\n

An article in the New York Times reveals that Google has been transcribing videos on YouTube so it can use the texts for training purposes. Google is not the only company taking this route:\u00a0 OpenAI has created a speech recognition tool called Whisper that lets it transcribe audio from YouTube videos.<\/p>\n\n\n\n

Other companies with large stores of user-generated content are realizing how valuable their assets could be. For example, Auto<\/a>m<\/a>attic<\/a> is planning to sell \u201cpublic content that\u2019s hosted on WordPress.com and Tumblr\u201d to AI companies. Video company Vimeo is considering doing the same, and is currently conducting a survey<\/a> about its users\u2019 views on such a move.<\/p>\n\n\n\n

Can You Avoid It?<\/strong><\/h2>\n\n\n\n

There are already articles on how to stop your online posts being used to train AI, but as one article on WIRED points out, it may be t<\/a>o<\/a>o late<\/a>: \u201cMany companies building AI have already scraped the web, so anything you\u2019ve posted is probably already in their systems.\u201d Given how much user-generated content some services store and its potential value, companies may start requiring that users consent to their words, images, videos, and audios files being licensed to generative AI companies for training purposes. This would be a terrible setback for the online privacy of billions of people.<\/p>\n\n\n\n

The fight back has already begun, in the EU at least. Privacy activist Max Schrems\u2019s noyb<\/a> organization has asked the Austrian data protection authority (DSB) to investigate OpenAI\u2019s data processing and the measures it\u2019s taken to ensure how personal data is handled in its LLM training sets. Noyb wants the DSB to order OpenAI to bring its processing in line with the EU\u2019s GDPR<\/a> privacy law and to impose a fine on the company \u201cto ensure future compliance<\/a>.\u201d If successful, this complaint is likely to impact most AI companies working with large language models in the EU.<\/p>\n","protected":false},"excerpt":{"rendered":"

In the 15 months since we first warned that generative AI would be terrible for privacy, the potential threats have become clearer. One example of the new problems generative AI brings is the increasing popularity of romantic chatbots, which can explicitly request \u2013 and have often obtained \u2013 highly intimate data. There\u2019s a new trend … Continue reading “Get Ready for Generative AI\u2019s Next Assault On Your Privacy”<\/span><\/a><\/p>\n","protected":false},"author":20,"featured_media":26094,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_stopmodifiedupdate":false,"_modified_date":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-27365","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"yoast_head":"\nGet Ready for Generative AI\u2019s Next Assault On Your Privacy<\/title>\n<meta name=\"description\" content=\"In the 15 months since we first warned that generative AI would be terrible for privacy, the potential threats have become clearer. 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