Request for Proposals (RFP)

CRAIG operates within the NSF Industry–University Cooperative Research Centers (IUCRC) Program, a proven framework for long-term collaboration among universities, industry, and government. Through this model, the Center combines NSF’s foundational support with direct industry and government agency investment to sustain applied, industry- and government-driven research, ensure transparent governance through its Industry Advisory Board (IAB), and maintain the infrastructure needed for cross-site coordination, data sharing, and workforce development.

Artificial intelligence is embedded throughout the economy.  CRAIG’s founding Industry Members accordingly come from the  technology, automotive, frontier AI,  pharmaceutical, insurance, health care, manufacturing, and financial sectors. Additional members and partners are expected to join as the Center’s portfolio expands through new projects and initiatives.  “Industry members” may include both private and public sector organizations.

In an IUCRC, academics work with industry members to pursue breakthrough research. They do this through a four-step, annual cycle: (1) academics and industry members meet to identify the most pressing knowledge gaps; (2) academics propose research projects to address these gaps; (3) industry members, sitting as the Industry Advisory Board, select projects for funding and fund the research; and (4) academics carry out the research to scholarly standards. CRAIG conducts pre-competitive research of shared interest to its industry members.

This Request for Proposals corresponds to Step 2 in the above annual cycle. It invites faculty members at the four CRAIG universities to propose research projects for CRAIG funding. The RFP seeks high-impact, interdisciplinary research projects that advance CRAIG’s mission and foster intra-university collaboration. The topics outlined in this RFP reflect research priorities identified through engagement with CRAIG industry members. Selected projects will form the initial portfolio of CRAIG-funded research, laying the foundation for a long-term program of collaborative research and innovation.

• Opportunities for interdisciplinary and cross-institutional partnerships
• Funding ($50,000-$75,000 for most projects) to support students, faculty time, and acquisition of data or other resources
• Direct engagement with industry members who can provide research support, access, and feedback.
• Placement of graduate students in cutting-edge responsible AI research
• Mentorship and internship opportunities for students
• A streamlined proposal process with rapid deployment of funds.

4/1/26                   Request for proposals released

4/27/26                 Deadline for interested faculty to submit a brief proposal

5/11/26                 The CRAIG Core Leadership Group and industry members select 8-10 finalists, provide feedback to these finalists, and assist them in identifying a collaborator at another partner university

5/20/26                 Finalists submit final proposals

6/4-5/26                Finalists present a short (20 minute) pitch presentation at the annual IAB meeting to be held at Ohio State (in person presentations encouraged)

6/15/26                 As per standard IUCRC rules, Industry Advisory Board members select the proposals that will receive funding

July 2026             Research funding allocated

Aug-Sept 2026    Research projects launched

Proposals will be reviewed by the CRAIG Core Leadership Team and IAB Members. Evaluations will be based on the following criteria:

• Relevance to CRAIG Themes: Alignment with one or more of CRAIG’s core research themes and motivating questions.
• Responsiveness to Industry Needs: Demonstrated practical value proposition for CRAIG Industry Members and partners.
• Feasibility: Clarity and realism of goals, methods, timeline, and budget, including the ability to deliver actionable insights and outputs at regular intervals throughout the first year.
• Collaboration and Teaming: Strength of cross-disciplinary and cross-site partnerships, as well as engagement with industry champions.
• Workforce Development: Inclusion and mentorship of students or trainees through participation in the research.
• Vision for Impact: Potential for real-world adoption, scalability, or pilot implementation through CRAIG’s networks.

 AI Auditing

Can one develop a continuous, lifecycle‑wide auditing approach spanning both design‑phase process audits and post‑deployment impact audits of modern (agentic) black-box/white-box AI systems?  Can such approaches ensure that generative and agentic AI systems operate in accordance with legal fairness requirements?

AI Safety and Alignment

Can we enhance AI systems to improve alignment and safety through continuous oversight, including explainability- and interpretability-driven RAI goal verification, adversarial stress-testing, and post-deployment monitoring? 

AI Security and Red-Teaming

Can continuous, multi‑modal red‑teaming, spanning automated adversarial generation, agent‑driven exploit discovery, and human probing (e.g., jailbreaks, prompt injection), significantly improve an AI system’s, including an agentic AI system’s, resilience to security vulnerabilities post-deployment? 

AI Privacy and Organizational Data Sovereignty

How can we implement privacy-by-design safeguards and federated AI systems to prevent data breaches and misuse and support organizational data sovereignty and machine unlearning requirements?

Balancing governance and innovation

Do responsible AI governance implementation, and AI innovation, conflict with one another? Or can responsible AI governance promote innovation and competitiveness?  How can an organization best implement a risk-based approach to AI governance?  

Evidence-based governance best practices

What standards, indices, metrics, evaluation tools, and methodologies can we use to evaluate AI governance practices? Can we evaluate how these practices impact social and business performance and so identify evidence-based AI governance best practices? 

Governance programs 

How can AI governance best meet the challenges posed by the rapid growth, scale, evolution, and reach of AI systems?  How should existing governance structures and processes adapt?  What skills do employees need to integrate AI governance practices into their routines? How can organizations best engage stakeholders?

Organizational health

How does AI reshape organizational culture, workforce experiences and identity, reskilling/upskilling needs, and satisfaction, and how should organizations manage these impacts?

Regulation of AI 

How do existing regulatory regimes apply to AI?  What gaps do they leave and how should policymakers best fill them? Which legal and regulatory paradigms and approaches are most effective for promoting safe and responsible AI?   

Liability for AI-related injuries

How do courts and legislatures currently allocate liability for AI-related harms, including those caused by AI agents, among developers, vendors, and deployers?  How should they allocate this liability? How are the parties themselves currently allocating this liability through contract, and how should they do so? 

Implementation of responsible AI standards

How should organizations operationalize legal, industry, or other standards for responsible AI (e.g. explainability, accuracy, human accountability)? Are legal requirements for technological solutions feasible from a technical perspective?  If so, how should organizations meet them?

Ethical deliberation and accountability

How should organizations spot and decide AI-related ethical dilemmas? What structures, processes, and standards should they use for this purpose? How can organizations encourage and assign ethical responsibility and accountability?

Impacts on opportunities, skills development, and health

Does the rapid adoption of AI impact the opportunities, skills development, and physical and mental health of workers, students, and others? What long-term implications will this have for individuals, organizations and society? 

Education for the AI era

What should we teach students and workers so that they can thrive in the AI enabled economy, and how should we teach it?  What should we teach students and workers so that they can use AI responsibly and participate effectively in AI governance, and how should we teach it? 

Human-AI Teaming and Interaction 

How can we design AI systems that center human values, amplify human abilities, and support transparent, safe, and effective human–AI collaboration that works for organizations and their employees? How can interface design foster trust, accessibility, and performance?

Energy and environmental impacts

How can we best calculate AI systems’ impact on energy resources and the environment? Is it possible to reduce these impacts while maintaining AI performance and, if so, how can this be done? 

All proposals should demonstrate a credible pathway to impact by showing how the project will ultimately create value for CRAIG’s Industry Members and deliver measurable societal benefits. Proposals that align with CRAIG’s Priority Research Areas are more likely to create such value, and are encouraged.

Proposals may be submitted by faculty at The Ohio State, Baylor, Northeastern, and or Rutgers Universities. Cross-disciplinary, cross-institutional teams are strongly encouraged, and CRAIG will facilitate opportunities for joint engagement.

Principal Investigators (PIs) must be faculty or researchers eligible for PI status or equivalent under their home institution’s policies. Co-PIs and collaborators may include postdoctoral researchers or research staff with appropriate institutional approval. An individual may participate (as PI, Co-PI or collaborator) in at most two proposals in this RFP cycle. Furthermore, an investigator may serve as the lead PI for exactly one submission. We are looking for your best ideas.

Proposals may also identify external collaborators from other universities, industry, government agencies, or nonprofits. Such collaborators may participate intellectually in CRAIG projects but are not eligible to receive direct financial support from CRAIG funds unless they are approved by the IAB as Industry Members or official partners under the terms of the CRAIG Membership Agreement.

• Award Amounts: Average project awards will range from $50,000 to $75,000 in the first year. Exceptional projects demonstrating outstanding value to CRAIG members and a strong vision for broader impact may be considered for awards of up to $100,000.
• Duration: Projects are expected to be funded for a one-year term.
• Allowable Costs: Funding may be used for personnel (limited faculty support, postdoctoral researchers, graduate and undergraduate students), data acquisition, software, equipment, travel, stakeholder engagement, and other purposes with clear relevance to the conducting or  sharing of the research.
• Expectations: Projects should demonstrate a clear pathway to industry and societal impact, as described above. They should also create strong opportunities for student engagement through meaningful research, mentorship, and professional development activities that reinforce the Center’s workforce development goals.

Each project will be paired with an industry project advisor to provide academic researchers with useful feedback, ensure alignment with member priorities, facilitate data access and sharing, and strengthen the translation of research findings into practice. Projects that identify additional potential external partners, such as companies or public agencies, that may be eligible for CRAIG membership or partnership, are welcomed.

CRAIG provides targeted administrative and engagement support throughout the project lifecycle to help teams maximize impact, visibility, and alignment with Center goals. Available support includes:

• Industry Engagement: Assistance in identifying and connecting with industry partners and project champions to ensure relevance and facilitate applied collaboration.
• Student Involvement: Opportunities for student placements, internships, and participation in CRAIG research projects.
• Cross-Site Collaboration: Coordination with related efforts through bi-annual Center-wide meetings and cross-site research collaborations.

• April 1, 2026- RFP Released
• April 27, 2026, 11:59 PM- Proposals Submitted
• May 11, 2026- Finalists selected and feedback provided
• May 20, 2026 11:59 PM- Final Proposals Submitted
• June 4-5, 2026-  In-Person Project Pitch to IAB
• June 15, 2026- Funding Decisions Announced