We risk a deluge of AI-written'science' pushing corporate interests - here's what to do about it Back in the 2000s, the American pharmaceutical firm Wyeth was sued by thousands of women who had developed breast cancer after taking its hormone replacement drugs. Court filings revealed the role of "dozens of ghostwritten reviews and commentaries published in medical journals and supplements being used to promote unproven benefits and downplay harms" related to the drugs. Wyeth, which was taken over by Pfizer in 2009, had paid a medical communications firm to produce these articles, which were published under the bylines of leading doctors in the field (with their consent). Any medical professionals reading these articles and relying on them for prescription advice would have had no idea that Wyeth was behind them. The pharmaceutical company insisted that everything written was scientifically accurate and - shockingly - that paying ghostwriters for such services was common in the industry.

Peer review is a critical process for ensuring the integrity of published scientific research. Confidence in this process is predicated on the assumption that experts in the relevant domain give careful consideration to the merits of manuscripts which are submitted for publication. With the recent rapid advancements in large language models (LLMs), a new risk to the peer review process is that negligent reviewers will rely on LLMs to perform the often time consuming process of reviewing a paper. However, there is a lack of existing resources for benchmarking the detectability of AI text in the domain of peer review. To address this deficiency, we introduce a comprehensive dataset containing a total of 788,984 AI-written peer reviews paired with corresponding human reviews, covering 8 years of papers submitted to each of two leading AI research conferences (ICLR and NeurIPS). We use this new resource to evaluate the ability of 18 existing AI text detection algorithms to distinguish between peer reviews written by humans and different state-of-the-art LLMs. Motivated by the shortcomings of existing methods, we propose a new detection approach which surpasses existing methods in the identification of AI written peer reviews. Our work reveals the difficulty of identifying AI-generated text at the individual peer review level, highlighting the urgent need for new tools and methods to detect this unethical use of generative AI.

LinkedIn is the latest platform to hop on the generative AI bandwagon. The company is adding AI-powered "writing suggestions" and job descriptions to its service as it looks for new ways to infuse AI into its platform. The writing suggestions are meant to make it easier to fill out key profile fields that LinkedIn says can otherwise feel "daunting" to complete: the "about" and "headline" sections near the top of each profile. Now, with the new "enhance" tool, LinkedIn Premium subscribers can generate descriptions based on their experience. The company says the tool, which uses the same OpenAI models that power ChatGPT, is meant to preserve "your unique voice and style" and will draw from your job experience and skills, as well as LinkedIn's own "insights" into what makes a good profile.

We live in an age when AI technologies are booming, and the world has been taken by storm with the introduction of ChatGPT. ChatGPT is capable of accomplishing a wide range of tasks, but one that it does particularly well is writing articles. And while there are many obvious benefits to this, it also presents a number of challenges. In my opinion, the biggest hurdle that AI-generated written content poses for the publishing industry is the spread of misinformation. ChatGPT, or any other AI tool, may generate articles that may contain factual errors or are just flat-out incorrect.

Each of Burger King's new ads starts with an anachronistic burst of noise from a dial-up modem and a solemn warning: "This ad was created by artificial intelligence." Then, over shots of glistening burgers and balletic fries, a robotic-sounding narrator deploys exactly the sort of clunky grammar and conceptual malapropisms we expect from a dumb AI. "The chicken crossed the road to become a sandwich. Burger King encouraged the chicken," says the voice. In a press release, Burger King claims the videos are the work of a "new deep learning algorithm," but an article from AdAge makes it clear that humans -- not machines -- are responsible for the funnies. "Artificial intelligence is not a substitute for a great creative idea coming from a real person," Burger King's global head of brand marketing, Marcelo Pascoa, told the publication.