Machine learning has the potential to aid in mitigating the human effects of climate change. Previous applications of machine learning to tackle the human effects in climate change include approaches like informing individuals of their carbon footprint and strategies to reduce it. For these methods to be the most effective they must consider relevant social-psychological factors for each individual. Of social-psychological factors at play in climate change, affect has been previously identified as a key element in perceptions and willingness to engage in mitigative behaviours. In this work, we propose an investigation into how affect could be incorporated to enhance machine learning based interventions for climate change. We propose using affective agent-based modelling for climate change as well as the use of a simulated climate change social dilemma to explore the potential benefits of affective machine learning interventions. Behavioural and informational interventions can be a powerful tool in helping humans adopt mitigative behaviours. We expect that utilizing affective ML can make interventions an even more powerful tool and help mitigative behaviours become widely adopted.
Evaluating Personality Traits in Large Language Models: Insights from Psychological Questionnaires
School of Physics, Mathematics and Computing（物理、数学与计算学院）；University of Western Australia（西澳大学）；School of Computing（计算机学院）；School of Psychological Sciences（心理科学学院）
WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks
The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；Stanford University（斯坦福大学）；Colorado State University（科罗拉多州立大学）；NASA Marshall Space Flight Center（美国国家航空航天局马歇尔太空飞行中心）
LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies
organization= CogNosco Lab, Department of Psychology；Cognitive Science, University of Trento , city= Rovereto , country= Italy；organization= Institute of Information Science；Technologies ``Alessandro Faedo", National Research Council , country= Italy；organization= Thomas Lord Department of Computer Science, University of Southern California , city= Los Angeles , state= California , country= USA；organization= Department of Sociology；Social Research, University of Trento , city= Trento , country= Italy；organization= Centre for Behavioural；Implementation Sciences in Medicine (BISI), National University of Singapore , city= Singapore , country= Singapore
Why human-AI relationships need socioaffective alignment
Oxford Internet Institute, University of Oxford（牛津大学互联网研究所）；UK AI Safety Institute（英国人工智能安全研究所）；Google DeepMind（谷歌DeepMind）；Department of Experimental Psychology, University of Oxford（牛津大学实验心理学系）；Contextual AI（Contextual AI公司）；Meedan（米丹组织）
Earth Virtualization Engines -- A Technical Perspective
ETH Zurich（苏黎世联邦理工学院）；Max Planck Institute for Meteorology（马克斯·普朗克气象学研究所）；National Center for Atmospheric Research（国家大气研究中心）；Center for Earth System Research and Sustainability, Universität Hamburg (CEN)（汉堡大学地球系统研究与可持续性中心）；Swiss National Supercomputing Center (CSCS)（瑞士国家超级计算中心）；University of Bern（伯尔尼大学）；Commonwealth Scientific Industrial Research Organisation（英联邦科学与工业研究组织）；CSC - IT Center for Science（CSC - 芬兰信息技术中心）；University of Leeds（利兹大学）；University of Tennessee（田纳西大学）；German Climate Computing Center (DKRZ)（德国气候计算中心）；Massachusetts Institute of Technology（麻省理工学院）；NVIDIA Corporation（英伟达公司）；Federal Office of Meteorology and Climatology MeteoSwiss（瑞士联邦气象与气候办公室）；University of Reading（雷丁大学）
