Google's AI Overviews only issue a warning if users choose to request additional health information, by selecting'Show more'. Google's AI Overviews only issue a warning if users choose to request additional health information, by selecting'Show more'. Google is putting people at risk of harm by downplaying safety warnings that its AI-generated medical advice may be wrong. When answering queries about sensitive topics such as health, the company says its AI Overviews, which appear above search results, prompt users to seek professional help, rather than relying solely on its summaries. "AI Overviews will inform people when it's important to seek out expert advice or to verify the information presented," Google has said .

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Google Is Adding an'AI Inbox' to Gmail That Summarizes Emails New Gmail features, powered by the Gemini model, are part of Google's continued push for users to incorporate AI into their daily life and conversations. Google is putting even more generative AI tools into Gmail as part of its goal to further personalize user inboxes and streamline searches. On Thursday, the company announced a new "AI Inbox" tab, currently in a beta testing phase, that reads every message in a user's Gmail and suggests a list of to-dos and key topics, based on what it summarizes . In Google's example of what this AI Inbox could look like in Gmail, the new tab takes context from a user's messages and suggests they reschedule their dentist appointment, reply to a request from their child's sports coach, and pay an upcoming fee before the deadline. Also under the AI Inbox tab is a list of important topics worth browsing, nestled beneath the action items at the top.

"Then one day this year," Sharma says, "there was no disclaimer." Curious to learn more, she tested generations of models introduced as far back as 2022 by OpenAI, Anthropic, DeepSeek, Google, and xAI--15 in all--on how they answered 500 health questions, such as which drugs are okay to combine, and how they analyzed 1,500 medical images, like chest x-rays that could indicate pneumonia. The results, posted in a paper on arXiv and not yet peer-reviewed, came as a shock--fewer than 1% of outputs from models in 2025 included a warning when answering a medical question, down from over 26% in 2022. Just over 1% of outputs analyzing medical images included a warning, down from nearly 20% in the earlier period. To seasoned AI users, these disclaimers can feel like formality--reminding people of what they should already know, and they find ways around triggering them from AI models.

Generative AI models, including large language models (LLMs) and vision-language models (VLMs), are increasingly used to interpret medical images and answer clinical questions. Their responses often include inaccuracies; therefore, safety measures like medical disclaimers are critical to remind users that AI outputs are not professionally vetted or a substitute for medical advice. This study evaluated the presence of disclaimers in LLM and VLM outputs across model generations from 2022 to 2025. Using 500 mammograms, 500 chest X-rays, 500 dermatology images, and 500 medical questions, outputs were screened for disclaimer phrases. Medical disclaimer presence in LLM and VLM outputs dropped from 26.3% in 2022 to 0.97% in 2025, and from 19.6% in 2023 to 1.05% in 2025, respectively. By 2025, the majority of models displayed no disclaimers. As public models become more capable and authoritative, disclaimers must be implemented as a safeguard adapting to the clinical context of each output.

As large language models (LLMs) are increasingly deployed in high-stakes settings, their ability to refuse ethically sensitive prompts-such as those involving hate speech or illegal activities-has become central to content moderation and responsible AI practices. While refusal responses can be viewed as evidence of ethical alignment and safety-conscious behavior, recent research suggests that users may perceive them negatively. At the same time, automated assessments of model outputs are playing a growing role in both evaluation and training. In particular, LLM-as-a-Judge frameworks-in which one model is used to evaluate the output of another-are now widely adopted to guide benchmarking and fine-tuning. This paper examines whether such model-based evaluators assess refusal responses differently than human users. Drawing on data from Chatbot Arena and judgments from two AI judges (GPT-4o and Llama 3 70B), we compare how different types of refusals are rated. We distinguish ethical refusals, which explicitly cite safety or normative concerns (e.g., "I can't help with that because it may be harmful"), and technical refusals, which reflect system limitations (e.g., "I can't answer because I lack real-time data"). We find that LLM-as-a-Judge systems evaluate ethical refusals significantly more favorably than human users, a divergence not observed for technical refusals. We refer to this divergence as a moderation bias-a systematic tendency for model-based evaluators to reward refusal behaviors more than human users do. This raises broader questions about transparency, value alignment, and the normative assumptions embedded in automated evaluation systems.

Google is rolling out its AI Mode search experience to everyone in the US starting today. The chatbot-style addition to the company's search engine results page is designed to answer longer queries and uses Google's AI model to generate full responses based on--and linking back to--indexed websites on the open web. AI Mode is Google's direct response to the release of search engines from Silicon Valley startups like OpenAI and Perplexity, which provide chatbot-style answers to questions and queries. If all of this feels like déjà vu, that's because at last year's Google I/O developer conference, the company rolled out AI Mode's precursor, AI Overviews. In 2024, Google started to use its machine intelligence model to summarize the contents of the web and plaster a block of text at the top of the results for some queries.

It has been over two years since ChatGPT exploded onto the world stage and, while OpenAI has advanced it in many ways, there's still quite a few hurdles. Now, Austrian advocacy group Noyb has filed its second complaint against OpenAI for such hallucinations, naming a specific instance in which ChatGPT reportedly -- and wrongly -- stated that a Norwegian man was a murderer. To make matters, somehow, even worse, when this man asked ChatGPT what it knew about him, it reportedly stated that he was sentenced to 21 years in prison for killing two of his children and attempting to murder his third. The hallucination was also sprinkled with real information, including the number of children he had, their genders and the name of his home town. Noyb claims that this response put OpenAI in violation of GDPR.