[论文解读] Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives

Kwok Pan Chun, Thanti Octavianti+25 more|arXiv (Cornell University)|Sep 20, 2024

Insurance and Financial Risk Management被引用 5

一句话总结

本文主张打破学科间的壁垒，以将 AI/大数据 整合到灾害风险降低中，提出在法律、环境与社会方面的原则性、跨学科方法，以实现安全且可信的 AI 驱动的灾害管理。

ABSTRACT

Managing complex disaster risks requires interdisciplinary efforts. Breaking down silos between law, social sciences, and natural sciences is critical for all processes of disaster risk reduction. This enables adaptive systems for the rapid evolution of AI technology, which has significantly impacted the intersection of law and natural environments. Exploring how AI influences legal frameworks and environmental management, while also examining how legal and environmental considerations can confine AI within the socioeconomic domain, is essential. From a co-production review perspective, drawing on insights from lawyers, social scientists, and environmental scientists, principles for responsible data mining are proposed based on safety, transparency, fairness, accountability, and contestability. This discussion offers a blueprint for interdisciplinary collaboration to create adaptive law systems based on AI integration of knowledge from environmental and social sciences. Discrepancies in the use of language between environmental scientists and decision-makers in terms of usefulness and accuracy hamper how AI can be used based on the principles of legal considerations for a safe, trustworthy, and contestable disaster management framework. When social networks are useful for mitigating disaster risks based on AI, the legal implications related to privacy and liability of the outcomes of disaster management must be considered. Fair and accountable principles emphasise environmental considerations and foster socioeconomic discussions related to public engagement. AI also has an important role to play in education, bringing together the next generations of law, social sciences, and natural sciences to work on interdisciplinary solutions in harmony.

研究动机与目标

• 推动在灾害风险降低中，法律、社会科学和自然科学之间跨学科协作的需求。
• 探讨 AI 如何影响法律框架和环境管理，以及法律约束如何塑造 AI 的使用。
• 提出在灾害情境中负责任的数据挖掘的原则性、共同制定的指南。
• 提供一个将环境与社会科学知识整合到适应性法律体系的蓝图。

提出的方法

• 在共同生产的综述中综合律师、社会科学家与环境科学家的见解。
• 将安全、透明、公平、问责和可质疑性确认为负责任的 AI/数据做法的核心原则。
• 讨论环境科学家与决策者之间的语言差异如何影响 AI 的有用性及法律考量。
• 在社交网络为灾害风险降低做出贡献时，分析隐私与责任的影响。
• 倡导以 AI 为基础的教育以连接法律、社会科学与自然科学，推动跨学科解决方案。

实验结果

研究问题

• RQ1 在现有法律与环境框架下，AI 与大数据如何转变灾害风险降低？
• RQ2 哪些跨学科原则能够确保在 AI 辅助的灾害管理中实现安全、透明、公平、问责和可质疑性？
• RQ3 科学家与决策者之间的语言与沟通差距如何影响 AI 在灾害中的应用的有效性与合法性？

主要发现

• AI 驱动的灾害风险降低需要打破法律、社会科学和自然科学之间的壁垒，以实现自适应系统。
• 与多学科共同生产获得了围绕安全、透明、公平、问责和可质疑性为核心的负责任数据挖掘原则。
• 环境科学家与决策者在用语上的差异阻碍了 AI 的有用性及对法律考量的框架。
• 当社交网络被用于灾害管理时，隐私和责任问题会出现。
• 公正且可问责的做法应将环境考量和公众参与纳入社会经济讨论。
• 利用 AI 的教育可以让未来一代在法律、社会科学和自然科学之间实现跨学科解决方案的团结。