Self-driving cars from Waymo, Zoox, and Tesla are studded with cameras and sensors. Some of them are pointing at riders. One afternoon this past summer, a self-driving Waymo pulled into a parking lot in San Mateo County, California, and stayed there until the police arrived. Local cops had gotten a 911 call from Waymo staffers--humans, it should be said --who were viewing the car's passengers through cameras installed inside the vehicle. It looked like two teens were shooting a black handgun out the window of the vehicle, playing with it and drinking between shots, a police report said.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Apple's $250M Siri settlement: How to claim up to $95 The next connected device could be the shirt you're wearing Hacker claims 7.49M customer records stolen from American utility company Is everyone bricking their phone to stop doomscrolling? The secret list that tells scammers you're an easy target How the'Squatter Hunter' is fighting home takeovers with tech AI robot may stop your dog from barking while you're gone His home changed hands for $5. Could it happen to your family?

Things to Do in L.A. AI is watching your spending and setting your prices accordingly. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search. Companies are increasingly using AI tools to sift through shoppers' personal data and tailor prices in real time, raising alarms over hidden algorithms that can exploit desperation, income and location.

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Quantifying our lives is easier than it's ever been. But a philosopher of games warns that external metrics and data can never capture what's truly important. There are plenty of useful things a metric can reveal. There are even more it can obscure or corrupt. It took me well over a decade of tracking my own life in ever greater detail to fully appreciate this duality, which probably reveals something about both me and the nature of measurement. Like a lot of people bitten by the self-quantifying bug, I initially started gathering personal data to pursue a nebulous collection of goals and desires.

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.