NSF-CSIRO: HCC: Small: From Legislations to Action: Responsible AI for Climate Change

NSF-CSIRO:HCC:小型:从立法到行动:负责任的人工智能应对气候变化

基本信息

  • 批准号:
    2302785
  • 负责人:
  • 金额:
    $ 30万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-05-15 至 2026-04-30
  • 项目状态:
    未结题

项目摘要

Climate change is a key threat in contemporary life. To curb and ultimately reverse climate change governments, organizations, and citizens around the world are taking actions to reduce the carbon footprint of human activity. Achieving these goals requires the effective creation and implementation of climate interventions, ranging from the individual to the national level. Yet developing effective climate change policies poses a significant challenge. Numerous policy options are possible, and policies interact with one another in unexpected ways. The task is further complicated by the fact that there are many sources of information about climate change, but they are often laden with jargon and may conflict with one another. The goal of this project is to develop responsible artificial intelligence (AI) to help stakeholders make sense of climate change related information, so that non-experts can collectively make sense of and implement effective policies. This encompasses a wide variety of possible AI-assisted tasks, ranging from summarizing the latest news reports to explaining the outcome of new legislation. In particular, we will focus on improving policymaking and public awareness by bridging the information gap between a wealth of publicly available information and a dearth of actionable insights. This will ultimately improve the effectiveness of climate change policies. The technical aims are structured along three thrusts: 1) understanding which tasks users are willing to delegate to AI and why; 2) developing a large-scale dataset of climate change related information, ranging from legislative texts from Australia and the US to news articles and social media; and 3) developing prototypes of AI systems to demonstrate the promise of responsible AI in promoting effective climate change policies.This is a joint project between U.S. and Australian researchers funded by the Collaboration Opportunities in Responsible and Equitable AI under the U.S. NSF and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
气候变化是当代生活中的一个主要威胁。为了遏制并最终扭转气候变化,世界各地的政府、组织和公民正在采取行动减少人类活动的碳足迹。要实现这些目标,就需要从个人到国家层面有效地制定和实施气候干预措施。然而,制定有效的气候变化政策是一项重大挑战。许多政策选项是可能的,政策以意想不到的方式相互作用。这项任务由于以下事实而更加复杂:关于气候变化的信息有许多来源,但这些来源往往充斥着行话,而且可能相互冲突。该项目的目标是开发负责任的人工智能(AI),帮助利益相关者理解气候变化相关信息,以便非专家能够共同理解和实施有效的政策。这包括各种可能的人工智能辅助任务,从总结最新的新闻报道到解释新立法的结果。特别是,我们将侧重于改善政策制定和公众意识,弥合丰富的公开信息与缺乏可采取行动的见解之间的信息差距。这将最终提高气候变化政策的有效性。技术目标是围绕沿着三个重点构建的:1)了解用户愿意委托人工智能完成哪些任务以及为什么; 2)开发一个大规模的气候变化相关信息数据集,从澳大利亚和美国的立法文本到新闻文章和社交媒体;和3)的方法开发人工智能系统的原型,以展示负责任的人工智能在促进有效的气候变化政策方面的承诺。这是一个联合项目美国和澳大利亚的研究人员之间的合作机会,在负责任和公平的AI在美国NSF和澳大利亚联邦科学和工业研究组织(CSIRO)资助。这个奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

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Chenhao Tan其他文献

A Tale of Two Communities: Characterizing Reddit Response to COVID-19 through /r/China_Flu and /r/Coronavirus
两个社区的故事:通过 /r/China_Flu 和 /r/Coronavirus 描述 Reddit 对 COVID-19 的反应
  • DOI:
    10.3233/faia200305
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    J. S. Zhang;Brian Keegan;Q. Lv;Chenhao Tan
  • 通讯作者:
    Chenhao Tan
Responsible Language Technologies: Foreseeing and Mitigating Harms
负责任的语言技术:预见和减轻危害
spanQuery-dependent cross-domain ranking in heterogeneous network/span
异构网络中依赖于查询的跨域排名
  • DOI:
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    2.7
  • 作者:
    Bo Wang;Jie Tang;Wei Fan;Songcan Chen;Chenhao Tan;Zi Yang
  • 通讯作者:
    Zi Yang
Query-dependent Cross Domain Ranking in Heterogenous Network.
异构网络中依赖于查询的跨域排名。
  • DOI:
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    2.7
  • 作者:
    Bo Wang;Jie Tang;Wei Fan;Songcan Chen;Chenhao Tan;Zi Yang
  • 通讯作者:
    Zi Yang
Science, AskScience, and BadScience: On the Coexistence of Highly Related Communities
Science、AskScience 和 BadScience:论高度相关社区的共存

Chenhao Tan的其他文献

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{{ truncateString('Chenhao Tan', 18)}}的其他基金

CRII: CHS: Harnessing Machine Learning to Improve Human Decision Making: A Case Study on Deceptive Detection
CRII:CHS:利用机器学习改善人类决策:欺骗检测案例研究
  • 批准号:
    2125113
  • 财政年份:
    2021
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
FAI: Towards Adaptive and Interactive Post Hoc Explanations
FAI:迈向自适应和交互式事后解释
  • 批准号:
    2040989
  • 财政年份:
    2021
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
AI-DCL: EAGER: Explanations through Diverse, Feasible, and Interactive Counterfactuals
AI-DCL:EAGER:通过多样化、可行和交互式反事实进行解释
  • 批准号:
    2125116
  • 财政年份:
    2021
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
CAREER: Harnessing Decision-focused Explanations as a Bridge between Humans and Artificial Intelligence
职业:利用以决策为中心的解释作为人类和人工智能之间的桥梁
  • 批准号:
    2126602
  • 财政年份:
    2021
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
CAREER: Harnessing Decision-focused Explanations as a Bridge between Humans and Artificial Intelligence
职业:利用以决策为中心的解释作为人类和人工智能之间的桥梁
  • 批准号:
    1941973
  • 财政年份:
    2020
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
CRII: CHS: Harnessing Machine Learning to Improve Human Decision Making: A Case Study on Deceptive Detection
CRII:CHS:利用机器学习改善人类决策:欺骗检测案例研究
  • 批准号:
    1849931
  • 财政年份:
    2019
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
AI-DCL: EAGER: Explanations through Diverse, Feasible, and Interactive Counterfactuals
AI-DCL:EAGER:通过多样化、可行和交互式反事实进行解释
  • 批准号:
    1927322
  • 财政年份:
    2019
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant

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