构建可信赖的辅助AI框架,提升人类决策能力。
Assistive AI for Augmenting Human Decision-making
- 以董事会代理机制确保AI决策可问责、可信可靠
- 融合隐私保护与安全机制,支持跨领域信任网络
- 适合法律、公共政策等需人机协同决策的场景
针对快速演进的恶意AI技术远超监管步伐的问题,本文提出首个辅助型AI框架,旨在增强人类决策能力。该框架基于隐私、问责与可信性原则,通过‘董事会’作为代理机构,集体保障AI辅助决策的可靠性、可追溯性及符合社会价值与法律标准。方法上强调信息源与信息内容的双重可信度,支持信息筛选与引导,使个体和群体能基于前沿AI技术做出知情决策。文中详述框架构成、运行逻辑及典型应用场景,证明其在保持人类监督前提下,有效促进人机协同判断,助力辨别现实与虚假,拓展监管边界。框架适用于法律、社会治理等高风险决策领域。
原文摘要 · Abstract (English)
Regulatory frameworks for the use of AI are emerging. However, they trail behind the fast-evolving malicious AI technologies that can quickly cause lasting societal damage. In response, we introduce a pioneering Assistive AI framework designed to enhance human decision-making capabilities. This framework aims to establish a trust network across various fields, especially within legal contexts, serving as a proactive complement to ongoing regulatory efforts. Central to our framework are the principles of privacy, accountability, and credibility. In our methodology, the foundation of reliability of information and information sources is built upon the ability to uphold accountability, enhance security, and protect privacy. This approach supports, filters, and potentially guides communication, thereby empowering individuals and communities to make well-informed decisions based on cutting-edge advancements in AI. Our framework uses the concept of Boards as proxies to collectively ensure that AI-assisted decisions are reliable, accountable, and in alignment with societal values and legal standards. Through a detailed exploration of our framework, including its main components, operations, and sample use cases, the paper shows how AI can assist in the complex process of decision-making while maintaining human oversight. The proposed framework not only extends regulatory landscapes but also highlights the synergy between AI technology and human judgement, underscoring the potential of AI to serve as a vital instrument in discerning reality from fiction and thus enhancing the decision-making process. Furthermore, we provide domain-specific use cases to highlight the applicability of our framework.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。