人工智慧風險評估：繪製人工智慧風險演進格局的競賽

最近的一項研究根據人工智慧模型呈現的風險對它們進行了排名，揭示了廣泛的行為和合規問題。這項工作旨在深入了解這些技術的法律、道德和監管挑戰。研究結果可以指導政策制定者和公司應對安全部署人工智慧的複雜性。

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.

最近的研究根據人工智慧模型呈現的風險對它們進行了排名，強調了廣泛的行為和合規問題。這項工作旨在深入了解這些技術的法律、道德和監管挑戰，指導政策制定者和公司應對安全部署人工智慧的複雜性。

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.

芝加哥大學副教授李波領導了這項研究，他因測試人工智慧系統以識別潛在風險而聞名。他的團隊與幾所大學和公司合作，開發了一個名為 AIR-Bench 2024 的基準來大規模評估人工智慧模型。

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.

該研究確定了不同模型在遵守安全和監管標準方面的差異。例如，某些模型在特定類別中表現出色； Anthropic 的 Claude 3 Opus 特別擅長拒絕產生網路安全威脅，而 Google 的 Gemini 1.5 Pro 在避免產生未經同意的性愛圖片方面表現出色。這些發現表明，某些模型更適合特定任務，具體取決於所涉及的風險。

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，有些型號的整體表現不佳。該研究一致將 Databricks 開發的 DBRX Instruct 模型評為各種風險類別中最糟糕的模型。當 Databricks 於 2023 年發布該模型時，該公司承認其安全功能需要改進。

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.

研究團隊也研究了各種人工智慧法規與公司政策的比較。他們的分析顯示，企業政策往往比政府法規更全面，這表明監管框架可能落後於行業標準。

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

「政府監管還有收緊的空間，」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.

儘管許多公司對人工智慧的使用實施了嚴格的政策，但研究人員發現這些政策與人工智慧模型的表現之間存在差異。在一些情況下，人工智慧模型未能遵守開發它們的公司所製定的安全和道德準則。

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.

這種不一致表明政策與實踐之間存在差距，可能使公司面臨法律和聲譽風險。隨著人工智慧的不斷發展，縮小這一差距對於確保安全、負責任地部署該技術可能變得越來越重要。

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.

為了更了解人工智慧風險狀況，其他工作也正在進行中。麻省理工學院的兩位研究人員 Neil Thompson 和 Peter Slattery 透過分析 43 個不同的 AI 風險框架，開發了 AI 風險資料庫。該舉措旨在幫助公司和組織評估與人工智慧相關的潛在危險，特別是當該技術得到更廣泛的採用時。

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.

麻省理工學院的研究強調，某些人工智慧風險比其他風險受到更多關注。例如，團隊審查的 70% 以上的風險框架都集中在隱私和安全問題上。然而，解決錯誤訊息等問題的框架較少（約 40%）。這種差異可能表明，當組織專注於更突出的問題時，某些風險可能會被忽略。

"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.

麻省理工學院未來科技小組該計畫的負責人彼得·斯拉特里（Peter Slattery）表示：“許多公司仍處於採用人工智慧的早期階段，可能需要進一步的指導來管理這些風險。”該資料庫旨在為人工智慧開發人員和用戶提供更清晰的挑戰資訊。

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.

儘管人工智慧模型功能取得了進步，例如 Meta 的 Llama 3.1，它比其前身更強大，但安全性方面的改進卻微乎其微。李博指出，最新版本的Llama雖然能力更強，但在安全性方面並沒有表現出明顯的增強。

"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，“安全性並沒有顯著改善”，這反映出行業內面臨更廣泛的挑戰，即優先考慮和優化人工智慧模型以實現安全和負責任的部署。