X-Industry

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 underground market for large illicit language models is lucrative, said academic researchers who called for better safeguards against artificial intelligence misuse.  Academics at the Indiana University Bloomington[1] identified 212 malicious LLMs on underground marketplaces from April through September 2024.  The financial benefit for the threat actor behind one of them, WormGPT, is calculated at US$28,000 over two months, underscoring the allure for harmful agents to break artificial intel