Convergence Accelerator Phase 1 (RAISE): Fostering a Diverse Artificial Intelligence (AI) Workforce

融合加速器第一阶段 (RAISE):培养多元化的人工智能 (AI) 劳动力

基本信息

  • 批准号:
    1937888
  • 负责人:
  • 金额:
    $ 54.45万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-09-01 至 2021-05-31
  • 项目状态:
    已结题

项目摘要

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/potential benefit of this Convergence Accelerator Phase I project is to uncover key factors in engaging underrepresented high school students in Artificial Intelligence (AI) topics that lead to long-term pursuit of careers with an ultimate goal of increasing diversity in AI and better meeting the growing workforce demands. This project will employ an "AI First" model that emphasizes the concept of code that produces itself from data - a departure from traditional CS education that emphasizes coding alone. Such a paradigm shift requires a multi-disciplinary approach. This proposal brings together experts in the field of Social Work, Computer Science, and STEM education outreach and leverages existing relationships between multiple universities and a nonprofit education organization. Through research studies that will uncover best practices in engaging students with AI and through complementary outreach efforts that will serve as test beds for research findings, this proposal will set the foundation for how to build a pipeline at the K-12 level to lead to a diverse and competitive AI workforce. The end goal is that this diverse workforce will create new products using AI with less potential for bias.This Convergence Accelerator Phase I project aims to address the lack of qualified and diverse candidate pool for the AI workforce sector. It also addresses the growing concern that lack of representation among the AI workforce is resulting in creation of technology that is more likely to exacerbate societal biases, including sexism and racism. The convergent research approach employed here involves: 1) refining primary research questions regarding K-12 engagement with AI curriculum and develop accompanying assessment tools to evaluate AI outreach programs; 2) identifying best practices in social work research to create a robust recruitment model that intentionally targets underrepresented groups in AI to education outreach opportunities; and 3) utilizing a social work framework to develop a model for a pilot internship program that connects students to employers and brings them further along in the pipeline to a career in AI. This research will result in the development of a Toolkit that will be used widely by K-12 practitioners to engage more students and thereby create and even stronger pipeline of talent to enter the AI workforce in the future.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.
NSF融合加速器支持以团队为基础的多学科努力,以应对国家重要性的挑战,并在不久的将来展示可交付成果的潜力。 这个融合加速器第一阶段项目的更广泛的影响/潜在利益是揭示让代表性不足的高中生参与人工智能(AI)主题的关键因素,这些主题导致长期追求职业生涯,最终目标是增加人工智能的多样性,更好地满足不断增长的劳动力需求。该项目将采用“AI First”模型,强调从数据中产生自身的代码概念-与传统的CS教育不同,传统的CS教育只强调编码。 这种范式转变需要采取多学科办法。该提案汇集了社会工作,计算机科学和STEM教育外展领域的专家,并利用了多所大学和非营利性教育组织之间的现有关系。通过研究发现让学生参与人工智能的最佳实践,并通过补充的外展工作作为研究结果的测试平台,该提案将为如何在K-12级别建立一个管道奠定基础,以形成多元化和有竞争力的人工智能劳动力。 最终目标是,这支多元化的员工队伍将使用人工智能创造新产品,减少偏见的可能性。这个融合加速器第一阶段项目旨在解决人工智能员工队伍缺乏合格和多元化候选人的问题。 它还解决了越来越多的担忧,即人工智能劳动力中缺乏代表性导致技术的创造更有可能加剧社会偏见,包括性别歧视和种族主义。 这里采用的融合研究方法包括:1)完善有关K-12参与AI课程的主要研究问题,并开发相应的评估工具来评估AI外展计划; 2)确定社会工作研究中的最佳实践,以创建一个强大的招聘模型,有意将AI中代表性不足的群体定位为教育外展机会;以及3)利用社会工作框架来开发试点实习计划的模型,将学生与雇主联系起来,并使他们沿着进入人工智能职业生涯。 这项研究将导致一个工具包的开发,将被K-12从业者广泛使用,以吸引更多的学生,从而创造甚至更强大的人才管道,以进入人工智能劳动力在未来。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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Augustin Chaintreau其他文献

Distributed caching over heterogeneous mobile networks
  • DOI:
    10.1007/s11134-012-9297-7
  • 发表时间:
    2012-04-20
  • 期刊:
  • 影响因子:
    0.700
  • 作者:
    Stratis Ioannidis;Laurent Massoulié;Augustin Chaintreau
  • 通讯作者:
    Augustin Chaintreau

Augustin Chaintreau的其他文献

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{{ truncateString('Augustin Chaintreau', 18)}}的其他基金

Spokes: MEDIUM: NORTHEAST: Collaborative Research: Data Science Foundry: A Collaborative Platform for Computational Social Science
辐条:媒介:东北:协作研究:数据科学铸造厂:计算社会科学协作平台
  • 批准号:
    1761810
  • 财政年份:
    2018
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Standard Grant
Student Travel Support for the COSN 2014 Conference
COSN 2014 会议学生旅行支持
  • 批准号:
    1456350
  • 财政年份:
    2014
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Standard Grant
Student Travel Support for the SIGCOMM 2013 Conference
SIGCOMM 2013 会议的学生旅行支持
  • 批准号:
    1341344
  • 财政年份:
    2013
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Standard Grant
CAREER: Banalytics: Behavioral Network Analytics with Data Transparency
职业:Banalytics:具有数据透明度的行为网络分析
  • 批准号:
    1254035
  • 财政年份:
    2013
  • 资助金额:
    $ 54.45万
  • 项目类别:
    Continuing Grant

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  • 批准号:
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