论文标题
安全：AI系统的社会和环境证书
SECure: A Social and Environmental Certificate for AI Systems
论文作者
论文摘要
在一个越来越多地由AI应用程序主导的世界中，研究的方面是这些渴望大量计算的碳和社会足迹，这些算法需要大量的计算以及用于培训和预测的数据。尽管在短期内有利可图，但这些实践是不可持续的，并且从数据使用和能源使用的角度来看是在社会上提取的。这项工作提出了一个由ESG启发的框架，结合了社会技术措施，以建立对生态社会负责的AI系统。该框架有四个支柱：计算有效的机器学习，联合学习，数据主权和利兹式证书。 计算效率的机器学习是使用压缩网络体系结构的使用，这些架构表现出准确性的边缘下降。联合学习通过使用在设备上闲置容量的计算负载的技术来增强第一支柱的影响。这与数据主权的第三个支柱配对，以通过基于使用的隐私和差异隐私等技术确保用户数据的隐私。最终的支柱将所有这些因素联系在一起，并以标准化的环境和社会影响以标准化的方式认证产品和服务，从而使消费者可以将购买与价值保持一致。
In a world increasingly dominated by AI applications, an understudied aspect is the carbon and social footprint of these power-hungry algorithms that require copious computation and a trove of data for training and prediction. While profitable in the short-term, these practices are unsustainable and socially extractive from both a data-use and energy-use perspective. This work proposes an ESG-inspired framework combining socio-technical measures to build eco-socially responsible AI systems. The framework has four pillars: compute-efficient machine learning, federated learning, data sovereignty, and a LEEDesque certificate. Compute-efficient machine learning is the use of compressed network architectures that show marginal decreases in accuracy. Federated learning augments the first pillar's impact through the use of techniques that distribute computational loads across idle capacity on devices. This is paired with the third pillar of data sovereignty to ensure the privacy of user data via techniques like use-based privacy and differential privacy. The final pillar ties all these factors together and certifies products and services in a standardized manner on their environmental and social impacts, allowing consumers to align their purchase with their values.
