When programmers encounter puzzling code, their brains react in measurable ways. Now, researchers have shown that large language models (LLMs) exhibit similar signs of confusion when reading the same code. In a study from Saarland University and the Max Planck Institute for Software Systems, scientists compared human brain activity with LLM uncertainty and found striking alignment. Wherever humans struggled, the models did too. This discovery, described in the paper “How do Humans and LLMs P
aiethics (14)
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
For over ten years, computer scientist Randy Goebel and his colleagues in Japan have been quietly conducting one of the most revealing experiments in artificial intelligence —a legal reasoning competition based on the Japanese bar exam. The challenge is to have AI systems retrieve relevant laws and then answer the core question at the heart of every legal case of whether the law was broken or not. That yes/no decision, it turns out, is where AI stumbles hardest. This struggle has profound impl
On 17 September 2025, the Las Vegas Metropolitan Police Department arrested a suspected Scattered Spider member linked to attacks on Las Vegas casinos for computer intrusion, extortion, and identity theft. Between August and October 2023, multiple Las Vegas casinos suffered network intrusions linked to the cybercrime group “Scattered Spider,” prompting an FBI investigation.
See: https://redskyalliance.org/xindustry/scattered-spider-s-devious-web
“Through the course of the investigation, detect
Organizations today are often ambivalent about agentic AI because of both its unpredictable failures and its potential use in cybercrime. Agentic systems are increasingly being given more control and are operating autonomously, taking on complex tasks and decision-making processes on behalf of users. These are often conducted with minimal human oversight, and agentic AI systems are interacting directly with enterprise systems to automate workflows. While this approach offers efficiency in ro
