We study the ability of language models to reason about appropriate information disclosure--a central aspect of the evolving field of agentic privacy. Whereas previous works have focused on evaluating a model's ability to align with human decisions, we examine the role of ambiguity and missing context on model performance when making information-sharing decisions. We identify context ambiguity as a crucial barrier for high performance in privacy assessments. By designing Camber, a framework for context disambiguation, we show that model-generated decision rationales can reveal ambiguities and that systematically disambiguating context based on these rationales leads to significant accuracy improvements (up to 13.3% in precision and up to 22.3% in recall) as well as reductions in prompt sensitivity. Overall, our results indicate that approaches for context disambiguation are a promising way forward to enhance agentic privacy reasoning.

Users Like It or Not, A.I. Is Part of Health Care Now There's a key thing to keep in mind if you ask a chatbot for medical advice. Asking a general-use chatbot for health help used to seem like a shot in the dark--just two years ago, a study found that ChatGPT could diagnose only 2 in 10 pediatric cases correctly. Among Google Gemini's early recommendations were eating one small rock a day and using glue to help cheese stick to pizza . Last year, a nutritionist ended up hospitalized after taking ChatGPT's advice to replace salt in his diet with sodium bromide. Now A.I. companies have begun releasing health-specific chatbots for both consumers and health care professionals.

Ever since Meta debuted its smart glasses back in 2021, concerns have been raised over their ability to film people without their knowledge. Now, two Harvard students have taken the device's privacy-invading capabilities even further – by building a modified version called'I-XRAY'. The creepy system uses AI and facial recognition software to instantly dox people's identities. In an astonishing clip, the students go up to random strangers and quickly identify their name and other personal details – including their home address, work history and even the names of parents. It's reminiscent of the Black Mirror episode, White Christmas, where a hopeless singleton uses an implant to instantly find online information about strangers.

This past spring, a man in Washington State worried that his marriage was on the verge of collapse. "I am depressed and going a little crazy, still love her and want to win her back," he typed into ChatGPT. With the chatbot's help, he wanted to write a letter protesting her decision to file for divorce and post it to their bedroom door. "Emphasize my deep guilt, shame, and remorse for not nurturing and being a better husband, father, and provider," he wrote. In another message, he asked ChatGPT to write his wife a poem "so epic that it could make her change her mind but not cheesy or over the top." The man's chat history was included in the WildChat data set, a collection of 1 million ChatGPT conversations gathered consensually by researchers to document how people are interacting with the popular chatbot.

Over 170 images and personal details of children from Brazil have been scraped by an open-source dataset without their knowledge or consent, and used to train AI, claims a new report from Human Rights Watch released Monday. The images have been scraped from content posted as recently as 2023 and as far back as the mid-1990s, according to the report, long before any internet user might anticipate that their content might be used to train AI. Human Rights Watch claims that personal details of these children, alongside links to their photographs, were included in LAION-5B, a dataset that has been a popular source of training data for AI startups. "Their privacy is violated in the first instance when their photo is scraped and swept into these datasets. And then these AI tools are trained on this data and therefore can create realistic imagery of children," says Hye Jung Han, children's rights and technology researcher at Human Rights Watch and the researcher who found these images.

Databases covering all individuals of a population are increasingly used for research and decision-making. The massive size of such databases is often mistaken as a guarantee for valid inferences. However, population data have characteristics that make them challenging to use. Various assumptions on population coverage and data quality are commonly made, including how such data were captured and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases. Record linkage often implies subtle technical problems, which are easily missed. We discuss a diverse range of misconceptions relevant for anybody capturing, processing, linking, or analysing population data. Remarkably many of these misconceptions are due to the social nature of data collections and are therefore missed by purely technical accounts of data processing. Many of these misconceptions are also not well documented in scientific publications. We conclude with a set of recommendations for using population data.

Players of online video games such as Roblox, Fortnite and Fifa are being warned to watch out for scammers, amid concerns that gangs are targeting the platforms. Multiplayer games boomed during the pandemic lockdowns as people turned to socialising in virtual spaces. One of the UK's biggest banks, Lloyds, is so concerned about how games are being used that it will this week launch a warning code for players, and a character to go with it. Its research found that a fifth of gamers had either been a victim of a gaming-related scam, or knew someone who had, but less than a third said they knew how to spot one. "Scammers are always looking for new ways to trick people out of their money, and the world of video games is no exception," said Philip Robinson, fraud prevention director at Lloyds.

For the past two years, the LA-based voice actor has run a sort of reverse call centre, deliberately ringing the people most of us hang up on – scammers who pose as tax agencies or tech-support companies or inform you that you've recently been in a car accident you somehow don't recall. When Okumura gets a scammer on the line, she will pretend to be an old lady, or a six-year-old girl, or do an uncanny impression of Apple's virtual assistant Siri. Once, she successfully fooled a fake customer service representative into believing that she was Britney Spears. "I waste their time," she explains, "and now they're not stealing from someone's grandma." Okumura is a "scambaiter" – a type of vigilante who disrupts, exposes or even scams the world's scammers.

Insurance is a serious and complex subject. When it comes to securing their lives, their health, and their finances from any possible eventuality, customers understandably want to leave no stone unturned. During the process of buying insurance, they require access to detailed information while evaluating multiple options, in order to make an informed decision. And they will also need constant post-purchase support when it comes to making inquiries about their policies or filing claims. Needless to say, insurance firms across the globe receive massive volumes of queries every day, from prospective customers looking to buy insurance, and existing customers looking for help.

A data leak by smart home device manufacturer Wyze left the personal details of 2.4 million users exposed on the internet for more than three weeks. Among the compromised information was user email addresses, WiFi network names, smart device details and the health statistics of a limited number of users. Founded by former Amazon employees, the Seattle, Washington-based firm specialises in inexpensive smart cameras, light bulbs, plugs and security devices. Wyze has now secured the database and forced users to reset their account passwords, as well as their connections with other services like Amazon's Alexa or Google assistant. A data leak by smart home device manufacturer Wyze left the personal details of 2.4 million users exposed on the internet for more than three weeks.