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
deeplearning (11)
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
The cybersecurity company ESET has disclosed that it discovered an artificial intelligence (AI)-powered ransomware variant codenamed PromptLock. Written in Golang, the newly identified strain uses the gpt-oss:20b model from OpenAI locally via the Ollama API to generate malicious Lua scripts in real-time. The open-weight language model was released by OpenAI earlier this month. "PromptLock leverages Lua scripts generated from hard-coded prompts to enumerate the local filesystem, inspect target
