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Researchers have developed a comprehensive taxonomy of AI risks, focusing on the legal, ethical, and regulatory compliance issues of various AI models. This effort, led by Bo Li and collaborators, includes a benchmark called AIR-Bench 2024, which evaluates the safety of large language models. The analysis reveals significant discrepancies between government regulations and company policies regarding AI safety.
The findings indicate that while some models, like Anthropics Claude 3 Opus, perform well in avoiding cybersecurity threats, others, such as Databricks' DBRX Instruct, scored poorly across the board. This highlights the need for companies to prioritize safety features in their AI models, especially as they deploy these technologies in sensitive areas like customer service. The ongoing evolution of AI necessitates continuous updates to risk assessments and safety measures.
• Researchers created a benchmark to assess AI model safety and compliance.
• Government regulations on AI are less comprehensive than company policies.
This taxonomy helps in understanding the potential legal and ethical issues that AI technologies may pose.
It provides insights into how different models perform in terms of specific risks.
The performance of these models is critical in applications such as customer service and content generation.
Its Claude 3 Opus model is noted for effectively avoiding cybersecurity threats.
Its DBRX Instruct model was highlighted for scoring poorly in safety assessments.
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