AI chatbots are giving out people's real phone numbers People report that their personal contact info was surfaced by Google AI--and there's apparently no easy way to prevent it. A Redditor recently wrote that he was "desperate for help": for about a month, he said, his phone had been inundated by calls from "strangers" who were "looking for a lawyer, a product designer, a locksmith." Callers were apparently misdirected by Google's generative AI. In March, a software developer in Israel was contacted on WhatsApp after Google's chatbot Gemini provided incorrect customer service instructions that included his number. And in April, a PhD candidate at the University of Washington was messing around on Gemini and got it to cough up her colleague's personal cell phone number. AI researchers and online privacy experts have long warned of the myriad dangers generative AI poses for personal privacy.

ID.me is a private identity verification company used across U.S. government sites. Here's what to know about its security, privacy tradeoffs and common scams to avoid.

ChatGPT's new privacy policy states how the company uses cookies for tracking, to turn free users into paying subscribers. OpenAI is ready to target free users of its services with advertisements around the web, based on what it knows about them. On Thursday, OpenAI sent an email to users laying out major changes to the AI company's privacy policy in the US. "We'll now use cookies to promote OpenAI products and services on other websites," reads the email sent on April 30. "This does not impact your conversations in ChatGPT. Your conversations with ChatGPT are private and are not shared with marketing partners."

Machine learning models are often personalized by using group attributes that encode personal characteristics (e.g., sex, age group, HIV status). In such settings, individuals expect to receive more accurate predictions in return for disclosing group attributes to the personalized model. We study when we can tell that a personalized model upholds this principle for every group who provides personal data. We introduce a metric called the benefit of personalization (BoP) to measure the smallest gain in accuracy that any group expects to receive from a personalized model. We describe how the BoP can be used to carry out basic routines to audit a personalized model, including: (i) hypothesis tests to check that a personalized model improves performance for every group; (ii) estimation procedures to bound the minimum gain in personalization. We characterize the reliability of these routines in a finite-sample regime and present minimax bounds on both the probability of error for BoP hypothesis tests and the mean-squared error of BoP estimates. Our results show that we can only claim that personalization improves performance for each group who provides data when we explicitly limit the number of group attributes used by a personalized model. In particular, we show that it is impossible to reliably verify that a personalized classifier with k 19 binary group attributes will benefit every group who provides personal data using a dataset of n = 8 109 samples - one for each person in the world.

Machine learning models are often personalized by usinggroup attributesthat encodepersonalcharacteristics(e.g.,sex,agegroup,HIVstatus). Insuchsettings, individuals expect to receive more accurate predictions in return for disclosing group attributes to the personalized model.

Freecash will actually pay money out to users but not for watching videos. This misleading marketing coincides with the app's rising popularity. I first encountered the Freecash app after clicking on a sponsored TikTok video with dubious claims. The advertisement didn't promote this app by name, rather it showed a young woman expressing her excitement about seemingly getting hired by TikTok at $35 an hour to watch videos on her "For You" page. When I tapped the link to "order now," it sent me to a website with TikTok and Freecash logos, featuring a download link for the Freecash app.

A child rights advocacy organisation in the United States is accusing Google of bypassing parental authority by allowing children to disable parental supervision over Google accounts after they turn 13. Melissa McKay, president of the Digital Childhood Institute, stated on LinkedIn that Google sent her 12-year-old an email that will unlock additional tools once he turns 13, posting screenshots of the email. Among the changes, once children turn the age of 13, they can turn off supervised experiences on YouTube and can add payment methods to Google Pay. Parents will no longer be able to block apps, turn on location sharing without the permission of the child user or block access to payment features. "Google is asserting authority over a boundary that does not belong to them. It reframes parents as a temporary inconvenience to be outgrown and positions corporate platforms as the default replacement," McKay said in a post on LinkedIn.

Machine learning models are often personalized by using group attributes that encode personal characteristics (e.g., sex, age group, HIV status). In such settings, individuals expect to receive more accurate predictions in return for disclosing group attributes to the personalized model. We study when we can tell that a personalized model upholds this principle for every group who provides personal data. We introduce a metric called the benefit of personalization (BoP) to measure the smallest gain in accuracy that any group expects to receive from a personalized model. We describe how the BoP can be used to carry out basic routines to audit a personalized model, including: (i) hypothesis tests to check that a personalized model improves performance for every group; (ii) estimation procedures to bound the minimum gain in personalization. We characterize the reliability of these routines in a finite-sample regime and present minimax bounds on both the probability of error for BoP hypothesis tests and the mean-squared error of BoP estimates. Our results show that we can only claim that personalization improves performance for each group who provides data when we explicitly limit the number of group attributes used by a personalized model. In particular, we show that it is impossible to reliably verify that a personalized classifier with $k \geq 19$ binary group attributes will benefit every group who provides personal data using a dataset of $n = 8\times10^9$ samples -- one for each person in the world.

We demonstrate the utility of this resource and accompanying metamodel through application to existing legal frameworks and industry standards, offering insights for comparative framework analysis. Applications include compliance policy enforcement, compliance monitoring, data discovery, and risk assessment.

Holiday travelers face increased scammer attacks using leaked personal data from airlines and hotels to send fake flight cancellations and payment requests.