In financial applications, regulations or best practices often lead to specific requirements in machine learning relating to four key pillars: fairness, privacy, interpretability and greenhouse gas emissions. These all sit in the broader context of sustainability in AI, an emerging practical AI topic. However, although these pillars have been individually addressed by past literature, none of these works have considered all the pillars. There are inherent trade-offs between each of the pillars (for example, accuracy vs fairness or accuracy vs privacy), making it even more important to consider them together. This paper outlines a new framework for Sustainable Machine Learning and proposes FPIG, a general AI pipeline that allows for these critical topics to be considered simultaneously to learn the trade-offs between the pillars better. Based on the FPIG framework, we propose a meta-learning algorithm to estimate the four key pillars given a dataset summary, model architecture, and hyperparameters before model training. This algorithm allows users to select the optimal model architecture for a given dataset and a given set of user requirements on the pillars. We illustrate the trade-offs under the FPIG model on three classical datasets and demonstrate the meta-learning approach with an example of real-world datasets and models with different interpretability, showcasing how it can aid model selection.
Digital Twin and Artificial Intelligence Incorporated With Surrogate Modeling for Hybrid and Sustainable Energy Systems
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Ahsanullah University of Science and Technology（阿赫桑努拉科技大学）；Indian Institute of Technology Delhi（印度理工学院德里分校）；University of Bristol（布里斯托大学）；Missouri University of Science and Technology（密苏里科技大学）
Toward Multiphysics-Informed Machine Learning for Sustainable Data Center Operations: Intelligence Evolution with Deployable Solutions for Computing Infrastructure
Artificial Intelligence-Assisted Energy and Thermal Comfort Control for Sustainable Buildings: An Extended Representation of the Systematic Review
Mohammed V University of Rabat（拉巴特穆罕默德五世大学）；MENTIS SA（MENTIS SA公司）；University of Nebraska at Kearney (UNK)（内布拉斯加大学凯尔尼分校）；Information Technology University（信息技术大学）；Alfaisal University（费萨尔国王大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Western Michigan University（西密歇根大学）；Alakhawayn University in Ifrane（伊夫兰阿赫瓦因大学）；University of Houston（休斯顿大学）
不同的因素，如热舒适度，湿度，空气质量和噪音有显着的综合影响的可接受性和质量的活动进行的建筑物居住者谁花了他们的大部分时间在室内。在所引用的因素中，热舒适性有助于人类的福祉，因为它与人体的温度调节有关。因此，创造热舒适和节能的环境在建筑物的设计中非常重要，因此供暖，通风和空调系统也非常重要。最近的工作已经针对更先进的控制策略，主要是基于人工智能，具有模仿人类行为的能力。本文系统地综述了建筑物内的智能控制策略，并探讨了它们在室内环境中平衡热舒适性和节能优化的能力。方法.系统的文献综述使用ACM数字图书馆，Scopus，Google Scholar，IEEE Xplore（IEOL），Web of Science和Science Direct（SDOL）以及手动搜索的其他来源检查了同行评审的研究作品。包含以下字符串术语：热舒适性、舒适温度、首选温度、智能控制、高级控制、人工智能、计算智能、建筑、室内和建筑环境。入选标准为：英国，研究监测，主要是在建筑物中的人体热舒适性和能源效率同时基于控制策略，使用智能方法。使用系统性综述和荟萃分析指南的首选报告项目。最初，产生了1，077篇文章，最终有120篇符合纳入标准并进行了审查。
Artificial Intelligence: Research Impact on Key Industries; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2020)
TriRhenaTech联盟展示了计划于2020年5月13日在卡尔斯鲁厄举行的取消的三国“上莱茵人工智能研讨会”的论文集。TriRhenaTech联盟是上莱茵河三国大都市区的大学网络，包括位于富特旺根、凯泽斯鲁滕、卡尔斯鲁厄和奥芬堡的德国应用科学大学，巴登-符腾堡州合作州立大学Loerrach，法国大学网络阿尔萨斯理工大学，瑞士国立大学（由14所工程、建筑和管理领域的“高等院校”组成）和瑞士西北应用科学与艺术大学（University of Applied Sciences and Arts Northwestern Switzerland）。该联盟的共同目标是加强知识、研究和技术的转移，以及学生的跨境流动。
Design of a dynamic and self adapting system, supported with artificial intelligence, machine learning and real time intelligence for predictive cyber risk analytics in extreme environments, cyber risk in the colonisation of Mars
University of Oxford（牛津大学）；Cisco Research Centre（思科研究中心）；Cardiff University（卡迪夫大学）；University of Warwick（华威大学）
Artificial Intelligence for Sustainability: Facilitating Sustainable Smart Product-Service Systems with Computer Vision
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University of Bayreuth（拜罗伊特大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Plansee SE（攀时股份公司）
An Enhanced Analysis of Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function
Al-Kitab University（阿尔基塔布大学）；Annai Velankanni College of Engineering（安奈维兰卡尼工程学院）；Saveetha School of Engineering（萨维塔工程学院）；Sri Eshwar College of Engineering（斯里埃什瓦尔工程学院）；Northern Technical University（北方技术大学）；University of Anbar（安巴尔大学）
Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models
Nanjing University of Information Science and Technology（南京信息工程大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Hongkong University of Science and Technology（香港科技大学）；Fudan University（复旦大学）
Artificial intelligence for artificial materials: moiré atom
The NSF AI Institute for Artificial Intelligence and Fundamental Interactions（美国国家科学基金会人工智能与基本相互作用人工智能研究所）；Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）
