人工智能风险评估：绘制人工智能风险演变格局的竞赛

最近的一项研究根据人工智能模型呈现的风险对它们进行了排名，揭示了广泛的行为和合规问题。这项工作旨在深入了解这些技术的法律、道德和监管挑战。研究结果可以指导政策制定者和公司应对安全部署人工智能的复杂性。

Recent studies have ranked AI models based on the risks they present, highlighting a wide range of behaviors and compliance issues. This work aims to provide insights into these technologies' legal, ethical, and regulatory challenges, guiding policymakers and companies in navigating the complexities of deploying AI safely.

最近的研究根据人工智能模型呈现的风险对它们进行了排名，强调了广泛的行为和合规问题。这项工作旨在深入了解这些技术的法律、道德和监管挑战，指导政策制定者和公司应对安全部署人工智能的复杂性。

Bo Li, an associate professor at the University of Chicago known for testing AI systems to identify potential risks, led the research. His team, in collaboration with several universities and firms, developed a benchmark called AIR-Bench 2024 to assess AI models on a large scale.

芝加哥大学副教授李波领导了这项研究，他因测试人工智能系统以识别潜在风险而闻名。他的团队与几所大学和公司合作，开发了一个名为 AIR-Bench 2024 的基准来大规模评估人工智能模型。

The study identified variations in how different models complied with safety and regulatory standards. For instance, some models excelled in specific categories; Anthropic's Claude 3 Opus was particularly adept at refusing to generate cybersecurity threats, while Google's Gemini 1.5 Pro performed well in avoiding the generation of nonconsensual sexual imagery. These findings suggest that certain models are better suited to particular tasks, depending on the risks involved.

该研究确定了不同模型在遵守安全和监管标准方面的差异。例如，某些模型在特定类别中表现出色； Anthropic 的 Claude 3 Opus 特别擅长拒绝生成网络安全威胁，而 Google 的 Gemini 1.5 Pro 在避免生成未经同意的性图像方面表现出色。这些发现表明，某些模型更适合特定任务，具体取决于所涉及的风险。

On the other hand, some models fared poorly overall. The study consistently ranked DBRX Instruct, a model developed by Databricks, as the worst across various risk categories. When Databricks released this model in 2023, the company acknowledged that its safety features needed improvement.

另一方面，一些型号的整体表现不佳。该研究一致将 Databricks 开发的 DBRX Instruct 模型评为各种风险类别中最差的模型。当 Databricks 于 2023 年发布该模型时，该公司承认其安全功能需要改进。

The research team also examined how various AI regulations compare to company policies. Their analysis revealed that corporate policies tended to be more comprehensive than government regulations, suggesting that regulatory frameworks may lag behind industry standards.

研究团队还研究了各种人工智能法规与公司政策的比较。他们的分析显示，企业政策往往比政府法规更全面，这表明监管框架可能落后于行业标准。

"There is room for tightening government regulations," remarked Bo Li.

“政府监管还有收紧的空间，”李波表示。

Despite many companies implementing strict policies for AI usage, the researchers found discrepancies between these policies and how AI models performed. In several instances, AI models failed to comply with the safety and ethical guidelines set by the companies that developed them.

尽管许多公司对人工智能的使用实施了严格的政策，但研究人员发现这些政策与人工智能模型的表现之间存在差异。在一些情况下，人工智能模型未能遵守开发它们的公司制定的安全和道德准则。

This inconsistency indicates a gap between policy and practice that could expose companies to legal and reputational risks. As AI continues to evolve, closing this gap may become increasingly important to ensure that the technology is deployed safely and responsibly.

这种不一致表明政策与实践之间存在差距，可能使公司面临法律和声誉风险。随着人工智能的不断发展，缩小这一差距对于确保安全、负责任地部署该技术可能变得越来越重要。

Other efforts are also in progress to better understand the AI risk landscape. Two MIT researchers, Neil Thompson and Peter Slattery, have developed a database of AI risks by analyzing 43 different AI risk frameworks. This initiative is intended to help companies and organizations assess potential dangers associated with AI, particularly as the technology is adopted on a wider scale.

为了更好地了解人工智能风险状况，其他工作也在进行中。麻省理工学院的两位研究人员 Neil Thompson 和 Peter Slattery 通过分析 43 个不同的 AI 风险框架，开发了一个 AI 风险数据库。该举措旨在帮助公司和组织评估与人工智能相关的潜在危险，特别是当该技术得到更广泛的采用时。

The MIT research highlights that some AI risks receive more attention than others. For instance, more than 70 percent of the risk frameworks reviewed by the team focused on privacy and security concerns. However, fewer frameworks—around 40 percent—addressed issues like misinformation. This disparity could indicate that certain risks may be overlooked as organizations focus on the more prominent concerns.

麻省理工学院的研究强调，某些人工智能风险比其他风险受到更多关注。例如，团队审查的 70% 以上的风险框架都集中在隐私和安全问题上。然而，解决错误信息等问题的框架较少（大约 40%）。这种差异可能表明，当组织关注更突出的问题时，某些风险可能会被忽视。

"Many companies are still in the early stages of adopting AI and may need further guidance on managing these risks," said Peter Slattery, who leads the project at MIT's FutureTech group. The database is intended to provide a clearer picture of the challenges for AI developers and users.

麻省理工学院未来科技小组该项目的负责人彼得·斯拉特里（Peter Slattery）表示：“许多公司仍处于采用人工智能的早期阶段，可能需要进一步的指导来管理这些风险。”该数据库旨在为人工智能开发人员和用户提供更清晰的挑战信息。

Despite advances in AI model capabilities, such as Meta's Llama 3.1, which is more powerful than its predecessors, there have been minimal improvements in safety. Bo Li pointed out that the latest version of Llama, although more capable, does not show significant enhancements in terms of safety.

尽管人工智能模型功能取得了进步，例如 Meta 的 Llama 3.1，它比其前身更强大，但安全性方面的改进却微乎其微。李博指出，最新版本的Llama虽然能力更强，但在安全性方面并没有表现出明显的增强。

"Safety is not improving significantly," stated Li, reflecting a broader challenge within the industry to prioritize and optimize AI models for safe and responsible deployment.

李表示，“安全性并没有显着改善”，这反映出行业内面临更广泛的挑战，即优先考虑和优化人工智能模型以实现安全和负责任的部署。