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

随着人工智能(AI)越来越深入地融入现代工作场所的结构中，工作的性质和结构正在发生根本性的变化。这种整合在无处不在的人工智能改变工作场所的机会和围绕偏见、安全和隐私的新兴风险之间造成了紧张。目前，人工智能工具正在以异常快的速度发展，并部署在价值最大化先于监管的环境中。因此，下一代创新者需要一种新的培训。对于算法设计者来说，这意味着理解并敏感于他们的创作可能通过与社会技术生态系统中的用户交互以意想不到的方式运行的背景。对于系统设计人员来说，这意味着要充分了解人工智能工具是如何演变的，以重新想象任务和流程如何能够并且应该以充分利用人工智能工具的潜在力量的方式改变工作。这项授予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学生来满足这些需求，他们将准备好在未来的工作场所以积极影响社会的方式为人工智能做出趋同的研究贡献。该项目预计将培训120名学生，其中包括25名资助的博士生，主要服务于计算机科学和系统工程学科的学生，但与学生和教职员工在法律、媒体、公共事务、公共卫生和国际事务方面有密切的互动。该NRT旨在教育有能力“共同设计”人工智能算法和工作系统的研究人员，在新系统的能力和他们的“可信度”方面释放新的机会。为了实现这一点，该教育计划旨在灌输以下内容：1)在遥远的学科之间架起一座桥梁。通过新颖的入职程序和与同行、导师和行业分享跨学科参与的经验，该项目将培养跨学科的“梳状”学者，他们在人工智能算法或工作系统设计方面拥有坚实的基础，也愿意深入参与其他学科领域，这些领域是他们选择的研究问题的基础。2)欣赏结合情境解决问题的能力。当善意的系统在部署后发展时，就会出现重要的问题。NRT很早就强调了背景，而且经常是在研究正在形成的时候。夏令营将促进研究问题的形成，使广泛的利益相关者能够进行早期的反馈和测试。此外，通过让来自不同项目的学生通过入职序列获得专业证书，将创造从理论到实践再回来的自然交叉授粉的非正式机会。3)整体职业认同。尽管许多博士项目开始搭建脚手架来支持“软技能”，但这通常是与核心项目元素分开进行的。该计划的战略是使沟通、领导、团队合作和道德成为每个计划要素的核心。训练营和研讨会还将为学生提供有组织的机会来学习、实践和强化他们的战略，例如，在背景下参与道德建设。4)重视不同视角的决策。人工智能算法往往会加剧现有的偏见，这使得在决策中引入不同的视角以减轻意外后果变得尤为重要。目前，人工智能的采用是由一个相对同质的群体推动的。有必要增加代表不足的群体的参与，并让学生认识到在这一过程早期引入不同观点的价值。NSF研究培训(NRT)计划旨在鼓励开发和实施大胆的、具有潜在变革意义的STEM研究生教育培训模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科或趋同研究领域对STEM研究生进行有效培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.