While much research has shown the presence of AI's "under-the-hood" biases (e.g., algorithmic, training data, etc.), what about "over-the-hood" inclusivity biases: barriers in user-facing AI products that disproportionately exclude users with certain problem-solving approaches? Recent research has begun to report the existence of such biases -- but what do they look like, how prevalent are they, and how can developers find and fix them? To find out, we conducted a field study with 3 AI product teams, to investigate what kinds of AI inclusivity bugs exist uniquely in user-facing AI products, and whether/how AI product teams might harness an existing (non-AI-oriented) inclusive design method to find and fix them. The teams' work resulted in identifying 6 types of AI inclusivity bugs arising 83 times, fixes covering 47 of these bug instances, and a new variation of the GenderMag inclusive design method, GenderMag-for-AI, that is especially effective at detecting certain kinds of AI inclusivity bugs.

Over 200 contractors who work on improving Google's AI products, including Gemini and AI Overviews, have been laid off, sources say. Workers enter a building on the Google headquarters campus on July 23, 2025, in Mountain View, California. More than 200 contractors who worked on evaluating and improving Google's AI products have been laid off without warning in at least two rounds of layoffs last month. The move comes amid an ongoing fight over pay and working conditions, according to workers who spoke to WIRED. In the past few years, Google has outsourced its AI rating work--which includes evaluating, editing, or rewriting the Gemini chatbot's response to make it sound more human and "intelligent"--to thousands of contractors employed by Hitachi-owned GlobalLogic and other outsourcing companies.

AI models are trained on vast swathes of data from every corner of the internet, by humans. AI models are trained on vast swathes of data from every corner of the internet, by humans. How thousands of'overworked, underpaid' humans train Google's AI to seem smart In the spring of 2024, when Rachael Sawyer, a technical writer from Texas, received a LinkedIn message from a recruiter hiring for a vague title of writing analyst, she assumed it would be similar to her previous gigs of content creation. On her first day a week later, however, her expectations went bust. Instead of writing words herself, Sawyer's job was to rate and moderate the content created by artificial intelligence. The job initially involved a mix of parsing through meeting notes and chats summarized by Google's Gemini, and, in some cases, reviewing short films made by the AI.

At least 15 million videos have been snatched by tech companies. Listen to more stories on the Noa app. W hen Jon Peters uploaded his first video to YouTube in 2010, he had no idea where it would lead. He was a professional woodworker running a small business who decided to film himself making a dining table with some old legs he had found in a barn. It turned out that people liked his candid style, and as he posted more videos, a fan base began to grow.

A car that accelerates instead of braking every once in a while is not ready for the road. A faucet that occasionally spits out boiling water instead of cold does not belong in your home. Working properly most of the time simply isn't good enough for technologies that people are heavily reliant upon. And two and a half years after the launch of ChatGPT, generative AI is becoming such a technology. Even without actively seeking out a chatbot, billions of people are now pushed to interact with AI when searching the web, checking their email, using social media, and online shopping.

On a Wednesday morning last month, I thought, just for a second, that AI was going to kill me. I had hailed a self-driving Waymo to bring me to a hacker house in Nob Hill, San Francisco. Just a few blocks from arrival, the car lurched toward the other lane--which was, thankfully, empty--and immediately jerked back. That sense of peril felt right for the moment. As I stepped into the cab, Federal Reserve Chair Jerome Powell was delivering a speech criticizing President Donald Trump's economic policies, and in particular the administration's sweeping on-again, off-again tariffs. A day earlier, the White House had claimed that Chinese goods would be subject to overall levies as high as 245 percent when accounting for preexisting tariffs, and the AI giant Nvidia's stock had plummeted after the company reported that it expected to take a quarterly hit of more than 5 billion for selling to China.

After publishing my third book in early April, I kept encountering headlines that made me feel like the protagonist of some Black Mirror episode. "Vauhini Vara consulted ChatGPT to help craft her new book'Searches,'" one of them read. "To tell her own story, this acclaimed novelist turned to ChatGPT," said another. "Vauhini Vara examines selfhood with assistance from ChatGPT," went a third. The publications describing Searches this way were reputable and fact-based.

For more than two years, every new AI announcement has lived in the shadow of ChatGPT. No model from any company has eclipsed or matched that initial fever. But perhaps the closest any firm has come to replicating the buzz was this past February, when OpenAI first teased its video-generating AI model, Sora. Tantalizing clips--woolly mammoths kicking up clouds of snow, Pixar-esque animations of adorable fluffy critters--promised a stunning future, one in which anyone can whip up high-quality clips by typing simple text prompts into a computer program. But Sora, which was not immediately available to the public, remained just that: a teaser.

Tech solutionism, as identified by Moss and Metcalf,7 is the notion that all problems have tractable technical fixes. We see variants in the naming and definition of this phenomenon: the technology imperative,8 or "the underlying technocratic philosophy of inevitability",4 or even old-fashioned technocracy itself. All versions designate a confident deployment of technology to solve a non-technical problem, with costs and other drawbacks reduced to secondary consideration. A certain Tech Leader promotes a new startup, Sunshine, thus: "… by applying AI … you can both solve valuable problems and you can give people back time. You can also build their confidence in AI."6

Rumors emerged last year of a collaboration between former Apple designer Jony Ive and OpenAI CEO Sam Altman, but the two have until now kept quiet about it. In a profile by The New York Times that was published this weekend, though, Ive confirms his company LoveFrom is leading the design on an AI product being built with Altman. Also on board are Tang Tan and Evans Hankey, both of whom held big design roles at Apple. There's so far a team of about 10 employees involved with the project, based in a San Francisco office building that's one of several properties Ive has purchased on a single city block, according to the Times. But we still don't know much about the product they're working on.