用算法量化AI可信度,融合伦理原则与技术评估。
Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems
- 结合伦理指南与页面排名算法,构建可量化的可信度评估框架。
- 通过算法减少主观自评依赖,实现对AI系统的整体可信度评估。
- 适合关注AI伦理治理与可信评估的研究者与实践者。
人工智能技术因其广泛的社会渗透和巨大影响力,成为人类制造物中最具复杂性的挑战之一,既带来显著效益,也伴随潜在风险。尽管其他技术也可能造成重大危害,但AI的普遍应用使其社会影响尤为深远。其系统复杂性与强大能力易导致人类对其运行过程失去直接掌控或理解。为应对这些风险,学界提出了多种理论工具与指导原则,同时开发了旨在保障可信AI的技术工具。前者虽具全局视野,却缺乏量化手段;后者虽能实现量化,却仅聚焦于可信AI的特定方面。本文提出一种新方法,将可信AI的伦理要素与PageRank及TrustRank算法相结合,构建兼顾理论深度与量化能力的评估框架,以降低当前领域普遍存在的主观自评偏差。实证表明,该方法可在保持理论完整性的同时,为AI系统提供定量可信度分析,实现全面且可操作的评估。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) technology epitomizes the complex challenges posed by human-made artifacts, particularly those widely integrated into society and exerting significant influence, highlighting potential benefits and their negative consequences. While other technologies may also pose substantial risks, AI's pervasive reach makes its societal effects especially profound. The complexity of AI systems, coupled with their remarkable capabilities, can lead to a reliance on technologies that operate beyond direct human oversight or understanding. To mitigate the risks that arise, several theoretical tools and guidelines have been developed, alongside efforts to create technological tools aimed at safeguarding Trustworthy AI. The guidelines take a more holistic view of the issue but fail to provide techniques for quantifying trustworthiness. Conversely, while technological tools are better at achieving such quantification, they lack a holistic perspective, focusing instead on specific aspects of Trustworthy AI. This paper aims to introduce an assessment method that combines the ethical components of Trustworthy AI with the algorithmic processes of PageRank and TrustRank. The goal is to establish an assessment framework that minimizes the subjectivity inherent in the self-assessment techniques prevalent in the field by introducing algorithmic criteria. The application of our approach indicates that a holistic assessment of an AI system's trustworthiness can be achieved by providing quantitative insights while considering the theoretical content of relevant guidelines.
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