Ever feel like your news feed is showing you only one side of the story? New research suggests that Large Language Models (LLMs), now increasingly used to curate news feeds, may be amplifying human-like biases. This isn't just about getting repetitive recommendations; it's about the potential for LLMs to trap users in echo chambers, where their existing views are constantly reinforced, and differing perspectives are filtered out. How does this happen? Researchers explore various cognitive biases present in human decision-making that LLMs appear to inherit. For example, "anchoring bias" causes LLMs to overemphasize the first piece of news a user interacts with, influencing subsequent recommendations. Similarly, the way news is framed can manipulate our perception, a phenomenon known as "framing bias." LLMs, it turns out, are also vulnerable to framing, favoring dramatically worded articles over neutral ones. What's more, LLMs exhibit a "status quo bias," favoring information they’ve already encountered during training, potentially hindering access to fresh perspectives and diverse news sources. Finally, "group attribution bias" raises concerns about LLMs associating specific topics with particular demographic groups, potentially reinforcing harmful stereotypes. But there's hope! Researchers are exploring ways to mitigate these biases. One approach involves training LLMs on synthetic data designed to counteract the biases found in real-world data. Another method uses clever prompt engineering to guide LLMs toward self-correction by iteratively refining their outputs. Human feedback also plays a crucial role. Evaluators can flag biased recommendations, helping LLMs learn to prioritize fairness and objectivity. While LLMs offer promising advancements in personalized news delivery, this research highlights the urgent need to address their susceptibility to cognitive biases. The future of balanced and objective news consumption hinges on our ability to develop LLMs that promote diverse viewpoints and informed decision-making.