SECure：AI系统的社会和环境认证
SECure: A Social and Environmental Certificate for AI Systems
摘要：
在一个越来越被人工智能应用所主导的世界中，一个未被充分研究的方面是这些耗电算法的碳足迹和社会足迹，这些算法需要大量的计算和大量的数据来进行训练和预测。虽然这些做法在短期内有利可图，但从数据使用和能源使用的角度来看，这些做法是不可持续的，对社会是掠夺性的。这项工作提出了一个ESG启发的框架，结合了社会技术措施，以建立生态社会责任的人工智能系统。该框架有四个支柱：计算效率机器学习，联邦学习，数据主权和LEEDesque证书。计算效率高的机器学习是使用压缩网络架构，其精度略有下降。联邦学习通过使用跨设备上的空闲容量分配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相结合，通过基于使用的隐私和差分隐私等技术来确保用户数据的隐私。最后一个支柱将所有这些因素联系在一起，并以标准化的方式对产品和服务的环境和社会影响进行认证，使消费者能够将他们的购买与他们的价值观相结合。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
