TRAILS Seed Grants is a grant from Institute for Trustworthy AI in Law & Society (TRAILS) that funds interdisciplinary research projects integrating diverse stakeholders into AI development and governance to improve trustworthiness, accessibility, and efficacy in AI-infused systems.

Backed by a $20 million NSF/NIST award, TRAILS draws on four partner institutions — University of Maryland, George Washington University, Morgan State University, and Cornell University. Inaugural awards totaling over $1. 5 million supported eight projects ranging from AI smoking cessation chatbots to autism care robots.

Individual awards reach up to $100,000. Eligible applicants include faculty at affiliated universities. No current deadline is listed; future seed grant rounds are expected.

TRAILS Announces First Round of Seed Funding — NSF Institute for Trustworthy AI in Law & Society (TRAILS) TRAILS Announces First Round of Seed Funding Written By Maria Ann Herd UMD Assistant Professor of Information Studies Ge Gao (left) discusses two of her TRAILS seed funding projects with Managing Director Darren Cambridge (right).

The Institute for Trustworthy AI in Law & Society ( TRAILS ) has unveiled an inaugural round of seed grants designed to integrate a greater diversity of stakeholders into the AI development and governance lifecycle, ultimately creating positive feedback loops to improve trustworthiness, accessibility and efficacy in AI-infused systems. The eight grants announced on January 24, 2024—totaling just over $1.

5 million—were awarded to interdisciplinary teams of faculty associated with the institute. The projects include developing AI chatbots to assist with smoking cessation, designing animal-like robots to assist caregivers interacting with autistic children, and exploring how users interact with AI-generated language translation systems.

The projects fall under TRAILS’ broader mission to transform the practice of AI from one driven primarily by technological innovation to one that is driven by ethics, human rights, and participation from communities whose voices have previously been marginalized.

“At the speed with which AI is developing, our seed grant program will enable us to keep pace—or even stay one step ahead—by incentivizing cutting-edge research and scholarship that spans AI design, development and governance,” said Hal Daumé III , a professor of computer science at the University of Maryland and the director of TRAILS.

Launched in May 2023 with a $20 million award from the National Science Foundation (NSF) and the National Institute of Standards and Technology (NIST), TRAILS draws on the strengths of its four primary institutions: the University of Maryland’s expertise in computing and human-computer interaction, George Washington (GW) University’s strengths in systems engineering and in AI as it relates to law and governance, Morgan State University’s work in addressing bias and inequity in AI, and Cornell University’s research in human behavior and decision-making.

“NIST and NSF's support of TRAILS enables us to create a structured mechanism to reach across academic and institutional boundaries in search of innovative solutions,” said David Broniatowski , an associate professor of engineering management and systems engineering at George Washington University who leads TRAILS activities on the GW campus.

“Seed funding from TRAILS will enable multidisciplinary teams to identify opportunities for their research to have impact, and to build the case for even larger, multi-institutional efforts. ” The new seed grant program funds research and innovation that is centered around TRAILS’ primary research thrusts —participatory design, methods and metrics, evaluating trust, and participatory governance.

“Some of the funded projects are taking a fresh look at ideas we may have already been working on individually, and others are taking an entirely new approach to timely, pressing issues involving AI and machine learning,” said Virginia Byrne , an assistant professor of higher education & student affairs at Morgan State who leads TRAILS activities on campus and served on the seed grant review committee.

A second round of seed funding will be announced later this year, said Darren Cambridge, who was recently hired as managing director of TRAILS to lead its day-to-day operations. Projects selected in the first round are eligible for a renewal, while other TRAILS faculty—or any faculty member at the four primary TRAILS institutions—can submit new proposals for consideration, Cambridge said.

Ultimately, the seed funding program is expected to strengthen and incentivize other TRAILS activities that are now taking shape, including K–12 education and outreach programs, AI policy seminars and workshops on Capitol Hill, and multiple postdoc opportunities for early-career researchers.

“We want TRAILS to be the ‘go-to’ resource for educators, policymakers and others who are seeking answers and solutions on how to build, manage and use AI systems that will benefit all of society,” Cambridge said.

The eight projects selected for the first round of TRAILS seed-funding are: Chung Hyuk Park and Zoe Szajnfarber from GW and Hernisa Kacorri from UMD aim to improve the support infrastructure and access to quality care for families of autistic children by developing novel parent-robot teaming for the home.

In addition to advancing assistive technology while working with families of diverse racial, ethnic and socioeconomic backgrounds, they will assess the impact of teams to create more trust in human-robot collaborative settings. Huaishu Peng and Ge Gao from UMD will work with Malte Jung from Cornell to build trust in embodied AI systems that bridge the gap between computers and human physical senses.

The researchers will explore the use of small desktop robots—or even ones that traverse the human body—that convey or interpret nonverbal cues, such as nodding, between blind and sighted individuals, while also gaining a deeper understanding of both groups’ values concerning teamwork facilitated by embodied AI.

Marine Carpuat and Ge Gao from UMD will explore “mental models”—how humans perceive things—for language translation systems used by millions of people daily. They will focus on how individuals, depending on their language fluency and familiarity with the technology, make sense of their “error boundary”—that is, deciding whether an AI-generated translation is correct or incorrect.

The team will also develop innovative techniques to teach users how to improve their mental models as they interact with machine translation systems.

Hal Daumé III , Furong Huang and Zubin Jelveh from UMD and Donald Braman from GW will propose new philosophies grounded in law to conceptualize, evaluate and achieve “effort-aware fairness,” which involves algorithms for determining whether an individual or a group is discriminated against .

The researchers will develop new metrics, evaluate fairness of datasets, and design novel algorithms that enable AI auditors to uncover and potentially correct unfair decisions. Lorien Abroms and David Broniatowski from GW will recruit smokers to study the reliability of using generative chatbots, such as ChatGPT, as the basis for a digital smoking cessation program.

Additional work will examine the acceptability by smokers and their perceptions of trust in using this rapidly evolving technology for help to quit smoking. Adam Aviv from GW and Michelle Mazurek from UMD will examine bias, unfairness and untruths such as sexism, racism and other forms of misrepresentation that come out of certain AI and machine learning systems.

Though some systems have public warnings of potential biases, the researchers want to explore how users understand these warnings, if they recognize how biases may manifest themselves in the AI-generated responses, and how users attempt to expose, mitigate and manage potentially biased responses.

Susan Ariel Aaronson and David Broniatowski from GW will create a prototype of a searchable, easy-to-use website to enable policymakers to better utilize academic research related to trustworthy and participatory AI. The team will analyze research publications by TRAILS-affiliated researchers to ascertain which ones may have policy implications.

Then, each relevant publication will be summarized and categorized by research questions, issues, keywords, and relevant policymaking uses. The resulting database prototype will enable the researchers to test the utility of this resource for policymakers.

— Story by Tom Ventsias, University of Maryland Huang Daumé Broniatowski Byrne Szajnfarber Kacorri Feizi Brauneis Peng Gao Jung Carpuat Jelveh Braman Abroms Aviv Mazurek Aaronson Park AI-equipped PC will be One of the Biggest Changes for PCs in Decades Addressing Gaps in Data Governance to Promote Trust in Generative AI