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

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.