[论文解读] A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges

Zhenghua Chen, Min Wu+3 more|arXiv (Cornell University)|May 8, 2022

Smart Cities and Technologies被引用 10

一句话总结

本综述对可持续人工智能进行了全面的技术综述，重点聚焦于降低环境影响的节能与高效数据学习算法，以及实现负责任、可解释和稳健的AI以促进社会可持续性。该研究提出了一种面向能耗的AI设计框架，倡导模型共享与知识蒸馏，并强调可解释AI与主动安全措施作为下一代可持续AI系统的关键使能技术。

ABSTRACT

Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because of its promises to bring vast benefits for consumers and businesses, with considerable benefits promised in productivity growth and innovation. To date it has reported significant accomplishments in many areas that have been deemed as challenging for machines, ranging from computer vision, natural language processing, audio analysis to smart sensing and many others. The technical trend in realizing the successes has been towards increasing complex and large size AI models so as to solve more complex problems at superior performance and robustness. This rapid progress, however, has taken place at the expense of substantial environmental costs and resources. Besides, debates on the societal impacts of AI, such as fairness, safety and privacy, have continued to grow in intensity. These issues have presented major concerns pertaining to the sustainable development of AI. In this work, we review major trends in machine learning approaches that can address the sustainability problem of AI. Specifically, we examine emerging AI methodologies and algorithms for addressing the sustainability issue of AI in two major aspects, i.e., environmental sustainability and social sustainability of AI. We will also highlight the major limitations of existing studies and propose potential research challenges and directions for the development of next generation of sustainable AI techniques. We believe that this technical review can help to promote a sustainable development of AI R&D activities for the research community.

研究动机与目标

• 应对大规模AI模型训练带来的日益增长的环境与社会成本。
• 识别计算与数据高效学习算法的技术进展，以降低AI的碳足迹。
• 通过AI系统中的公平性、隐私保护与鲁棒性，探讨社会可持续性。
• 提出一种面向能耗的AI设计框架，综合考虑AI开发全周期的资源消耗。
• 突出可持续AI的研究空白与未来方向，强调跨学科创新的重要性。

提出的方法

• 提出一种面向能耗的AI设计框架，基于数据获取、表征学习、模型训练与部署各阶段的能耗与资源消耗，评估AI系统。
• 倡导模型共享与知识蒸馏，以减少大型模型的重复训练，实现小型学生模型的高效微调。
• 通过可视化与解释深度神经网络操作（如卷积层）推进可解释AI，提升模型透明度。
• 实施预判性AI安全策略，包括特权访问控制与差分隐私等隐私保护技术，如联邦学习。
• 将公平性与偏见检测集成到可解释模型中，实现模型公平性的自我诊断。
• 提出一种整体方法，结合技术创新与治理、透明度及问责机制，推动可持续AI发展。

实验结果

研究问题

• RQ1 计算与数据高效学习算法在多大程度上可降低大规模AI模型训练的环境成本？
• RQ2 哪些技术方法可实现模型共享与知识蒸馏，以最小化重复训练与能源消耗？
• RQ3 可解释AI技术在多大程度上可提升模型可解释性，并支持对公平性与偏见的自我诊断？
• RQ4 主动安全机制（如访问控制与联邦学习）如何增强AI的鲁棒性与隐私保护？
• RQ5 实现AI在环境与社会可持续性方面的关键研究挑战与未来方向是什么？

主要发现

• 训练单一大型Transformer模型可能排放高达626,155磅的二氧化碳，凸显了采用节能AI技术的紧迫性。
• 模型共享与知识蒸馏可通过复用预训练的大模型，避免从零开始训练，显著降低训练能耗。
• 可解释AI工具（如CNN Explainer）通过可视化卷积操作提升了可解释性，但实现完整的模型级理解仍具挑战。
• 面向能耗的AI设计鼓励在AI开发的每个阶段（从数据采集到部署）均考虑环境成本。
• 主动安全措施（包括特权访问管理与联邦学习）对于保护模型权重与训练数据免受对抗性威胁至关重要。
• 将公平性、隐私保护与鲁棒性整合到可解释模型中，可实现自我诊断，降低偏见或不安全AI系统的风险。