NRT-AI-FW-HTF: Co-Design of Trustworthy AI and Future Work Systems

NRT-AI-FW-HTF：值得信赖的人工智能和未来工作系统的协同设计

• 批准号: 2125677
• 负责人: Zoe Szajnfarber
• 金额: $ 300万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125677&HistoricalAwards=false
• 关键词: NRT; AI; FW; HTF; Co

The nature and structure of work are fundamentally changing as artificial intelligence (AI) becomes more deeply integrated within the structures of modern workplaces. This integration creates tension between the opportunities for ubiquitous AI to transform the workplace and emerging risks around bias, security, and privacy. Currently, AI tools are being developed at an unusually rapid pace, and deployed into environments where value maximization precedes regulation. The next generation of innovators accordingly needs a new kind of training. For algorithm designers, this means understanding and being sensitive to the context in which their creations may operate in unexpected ways through interaction with users in socio-technical ecosystems. For system designers, this means knowing enough about how AI tools are evolving to reimagine how tasks and processes could and should transform work in ways that fully leverage the potential power of AI tools. This National Science Foundation Research Traineeship (NRT) award to the George Washington University will address these needs by training doctoral students, master’s students, and graduate certificate students who will be prepared to make convergent research contributions to AI in the future workplace in a way that positively impacts society. The project anticipates training one-hundred and twenty (120) students, including twenty-five (25) funded Ph.D. trainees, primarily serving students in the discipline of computer science and systems engineering but with close interaction with the students and faculty in law, media, public affairs, public health, and international affairs.This NRT aims to educate researchers capable of “co-designing” AI algorithms and work systems to unlock new opportunities in both the capabilities of new systems and their “trustworthiness.” To accomplish this, the educational program aims to instill the following: 1) Comfort in bridging distant disciplines. Through novel onboarding sequences and shared experience of cross-disciplinary engagement with peers, mentors, and industry, the program will educate interdisciplinary, “comb-shaped” scholars who have a solid base in either AI algorithms or work system design and are also comfortable engaging deeply with other disciplinary areas fundamental to their chosen research problems. 2) Appreciation for contextually-embedded problem-solving. Important issues arise when well-intentioned systems evolve post-deployment. The NRT emphasizes context early and often as research is being formulated. Summer bootcamps will facilitate research problem formulation that enables early cycles of feedback and testing with a broad set of stakeholders. Additionally, by intertwining students from different programs by engaging them in a professional certificate through the onboarding sequences, informal opportunities will be created for natural cross-pollination from theory to practice and back. 3) Holistic professional identities. Although many Ph.D. programs are starting to build scaffolding to support “soft-skills,” this usually occurs separately from core program elements. This program’s strategy is to make communication, leadership, teamwork, and ethics central to each program element. The bootcamps and seminars will also provide structured opportunities for students to learn, practice, and reinforce their strategies, e.g., engaging with ethics in context. 4) Valuing diverse perspectives in decision-making. AI algorithms tend to exacerbate existing biases, making it especially important to bring diverse perspectives into decision-making to mitigate unintended consequences. Currently, AI adoption is being driven by a relatively homogenous group. There is a need to increase participation from underrepresented groups and expose students to the value of bringing in diverse perspectives early in the process. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.

随着人工智能（AI）变得更加深入地整合到现代工作场所的结构中，工作的性质和结构正在从根本上发生变化。这种整合在无处不在的AI改变工作场所的机会与围绕偏见，安全和隐私的新兴风险之间产生了紧张。当前，AI工具正在以异常快速的速度开发，并部署到了最大程度地位的环境中。因此，下一代创新者需要一种新的培训。对于算法设计师而言，这意味着要通过与社会技术生态系统中的用户互动来理解其创作可能以意想不到的方式运作的环境。对于系统设计师而言，这意味着足够了解AI工具如何发展以重新想象任务和过程如何并且应该以充分利用AI工具潜在力量的方式来改变工作。乔治华盛顿大学的国家科学基金会研究培训（NRT）奖将通过培训博士生，硕士学生和研究生证书的学生来满足这些需求，他们将准备以积极影响社会的方式在未来的工作场所向AI做出融合研究贡献。该项目预计将培训一百二十（120）名学生，包括25（25）个资助博士学位。学员，在计算机科学和系统工程学科中为学生提供服务，但与法律，媒体，公共事务，公共卫生和国际事务的学生和教师进行了密切的互动。此NRT旨在教育能够“共同设计” AI算法和工作系统的研究人员，以在新的系统和新的系统能力中释放新的机会，并具有新的系统和他们的“ Trust Worthorness”。为此，教育计划旨在灌输以下内容：1）在弥合纪律学科时感到舒适。通过新颖的入职序列和与同龄人，导师和行业跨学科互动的共享经验，该计划将教育跨学科的“梳状”学者，他们在AI算法或工作系统设计中具有坚实的基础，并且还舒适地与其他对他们所选择的研究问题基本的学科领域一起舒适地互动。 2）感谢上下文解决问题的问题。当良好的系统发展后部署后，就会出现重要的问题。 NRT早期强调上下文，并且经常随着研究的制定。夏季训练营将促进研究问题公式，以实现与大量利益相关者的反馈和测试的早期周期。此外，通过通过登机序列与不同计划的学生进行专业证书来交织，将为从理论到练习和返回的自然交叉授粉创造非正式的机会。 3）整体专业身份。虽然许多博士学位程序开始建立脚手架以支持“软技能”，这通常与核心程序元素分开。该计划的策略是使沟通，领导，团队合作和道德规范的核心核心。训练营和半岛人还将为学生提供结构化的机会，以学习，实践和加强他们的策略，例如在上下文中与道德互动。 4）在决策中重视潜水员的观点。 AI算法倾向于加剧现有的偏见，这使得将潜水员的观点带入决策以减轻意想不到的后果尤为重要。目前，AI的采用率是由一个相对同质的群体驱动的。有必要增加代表性不足的群体的参与，并使学生在此过程中提出潜水员观点的价值。 NSF研究训练（NRT）计划旨在鼓励开发和实施用于STEM研究生教育培训的大胆，新的潜在变革模型。该计划致力于通过全面的跨学科或收敛研究领域的STEM研究生进行有效培训，通过全面的培训模型，这些模型具有创新性，基于证据，并且与不断变化的劳动力和研究需求保持一致。该奖项反映了NSF的法定任务，并通过基金会的知识优点和广泛的影响来评估NSF的法定任务，并通过评估诚实地进行了支持。

项目成果

DP2-Pub: Differentially Private High-Dimensional Data Publication With Invariant Post Randomization

• DOI: 10.1109/tkde.2023.3265605
• 发表时间: 2022-08
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Honglu Jiang;Hao-Chun Yu;Xiuzhen Cheng;Jian Pei;Robert Pless;Jiguo Yu

Emotion and Virality of Food Safety Risk Communication Messages on Social Media

• DOI: 10.4148/1051-0834.2391
• 发表时间: 2021-09
• 期刊: Journal of Applied Communications
• 作者: X. Wang;Xiaoli Nan;S. Stanley;Yuan Wang;L. Waks;David A. Broniatowski

The Opportunists in Innovation Contests: Understanding Whom to Attract and How to Attract Them

创新竞赛中的机会主义者：了解吸引谁以及如何吸引他们

• DOI: 10.1080/08956308.2022.2132771
• 发表时间: 2023
• 期刊: Research-Technology Management
• 影响因子: 2.2
• 作者: Vrolijk, Ademir;Szajnfarber, Zoe
• 通讯作者: Szajnfarber, Zoe

Knowledge-Augmented Language Models for Cause-Effect Relation Classification

• DOI: 10.18653/v1/2022.csrr-1.6
• 发表时间: 2021-12
• 期刊: Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)
• 作者: Pedram Hosseini;David A. Broniatowski;Mona T. Diab

Understanding Post-Production Change and Its Implications for System Design: A Case Study in Close Air Support During Desert Storm

了解后期制作变化及其对系统设计的影响：沙漠风暴期间近距离空中支援案例研究

• 发表时间: 2022
• 期刊: Naval engineers journal
• 影响因子: 0.2
• 作者: Singh, Aditya;Szajnfarber, Zoe
• 通讯作者: Szajnfarber, Zoe