Making Rural Communities Visible in Artificial Intelligence Through Rural Proofing in Kansas and Beyond
A road can show connection, but not access. Rural communities might appear in data and public systems, yet still remain invisible when AI systems do not reflect distance, transportation barriers, service gaps, workforce constraints, smaller data sets, and local strengths. Rural proofing gives Kansas and other rural states a practical way to make these realities visible in the AI-driven decisions already shaping health and social services.
Artificial intelligence (AI) is increasingly shaping decisions across public health systems, including how needs are identified, how resources are distributed, and how services are delivered. As a result, AI will play an important role in the future of healthy rural communities. When designed and governed carefully, AI can improve access, resource planning, coordination, and service delivery. When rural contexts are overlooked, AI systems can reproduce uneven outcomes and risk deepening existing disparities. In rural areas, where health systems often operate with fewer providers, thinner infrastructure, and less margin for error (meaning fewer backup resources when something goes wrong), these risks can be especially significant.
This memo examines rural invisibility in AI-related health systems, defined as the underrepresentation of rural communities in data, system design, validation, and governance. It explains why these gaps matter and why AI should be developed, tested, and governed with rural communities in mind. The term “rural” can be defined in a variety of ways, but this memo leans on the shared understanding of rural places as those with fewer people, less population density, and greater distance to services. While each rural community has a different history, strengths, resources and challenges, this memo – and the concept of “rural-proofing”, explained within – recognizes there are many shared challenges commonly faced by rural communities.
At both the national and state levels, there is an opportunity for more intentional action to recognize rural invisibility in AI systems as a policy issue. States can position themselves as proactive leaders in rural AI governance by aligning with federal frameworks while developing practical, state-level approaches. Kansas can become a leader in developing and implementing practical rural-proofing approaches that can serve as a model for other rural states. To do so, the state should take five connected steps: 1) make rural context a required part of any Kansas AI task force; 2) require rural proofing before agencies adopt or expand high impact AI tools; 3) institutionalize rural listening through trusted local partners; 4) document the Kansas model as a public blueprint other states can adapt; and 5) build a statewide rural AI literacy framework for residents,students, frontline workers, and public agencies.
Challenges and Opportunities
Rural communities have strong social connectedness, local knowledge, community leadership, and deep relationships that support resilience and innovation. Yet, they often face lower population density, greater geographic dispersion, and more limited access to services and infrastructure. In these settings, AI decisions in one domain can quickly affect others, making locally grounded context and community-level oversight especially important. As AI adoption grows, its effects on rural communities reach well beyond any single tool or system. What’s at stake is broader: how rural needs are represented in data, who has a voice in how AI decisions are made and governed, and how the benefits and burdens of AI systems and infrastructure are distributed across communities. These dynamics raise important questions about whether AI systems adequately account for rural conditions, populations, and lived experiences.
Rural Invisibility in AI Systems
Rural invisibility in AI systems occurs when rural communities are underrepresented in the data, assumptions, design, validation, and governance that shape how systems are built and used. That can make rural needs harder to see and rural harms harder to detect. In practice, it means that AI systems may be built on assumptions that do not reflect rural realities, leaving rural communities overlooked in decisions about resources, services, and policy.
The body of evidence, including the 2025 scoping review, illustrates how this invisibility carries into practice. It highlights that rural AI research is underdeveloped and that models underperform in rural settings, and the consequences of those failures are rarely studied where they are felt most. As the 2025 National Rural Health Association policy brief notes the challenge is not simply whether rural systems use AI, but whether technologies reflect the realities of fragmented records, thin staffing, and delayed care pathways. When those realities remain invisible in design and implementation, the consequences can include missed, delayed, or incorrect diagnosis, misallocation of resources, and greater strain on rural providers.
Gaps in AI Governance Frameworks
It is important to assess how well current governance approaches perform across different contexts. Current AI governance frameworks, including the National Institute of Standards and Technology Artificial Intelligence Risk Management Framework and Organization for Economic Co-operation and Development (OECD), provide a strong foundation by emphasizing fairness, transparency, accountability, and risk mitigation, but they provide limited guidance on how to operationalize these principles in rural environments. These frameworks often do not fully account for differences across settings. For example, communities and organizations vary in data availability, institutional capacity, and service infrastructure. They also differ in their ability to evaluate and govern AI tools, especially when staffing, technical expertise, and resources are uneven. Most frameworks do not require testing across small or geographically distinct populations, which can make it harder to see how AI performs in rural areas and allow disparities to go unnoticed.
In addition, current frameworks do not specify how local knowledge, professional judgment, or community perspectives, particularly those from rural communities, should be incorporated into AI oversight and decision-making, which can both algorithmic invisibility and broader forms of rural invisibility in AI. While they emphasize stakeholder engagement, they leave implementation largely undefined, which can limit the ability to identify context-specific risk. These gaps also matter because AI already shapes public benefits, legal navigation, housing, and service coordination. When trained on data shaped by past inequities, AI can deepen disparities rather than reduce them. This is why AI governance must move beyond general principles and explicitly incorporate rural proofing, accountability, and meaningful community involvement.
Federal policy remains an important lever because it can help push state policy forward by signaling priorities, shaping governance expectations, and giving states a stronger foundation for action.Current federal guidance provides a foundation for responsible AI use but offers more limited practical direction for rural settings, where sparse data, limited staffing, and fragmented service systems can affect how AI works in practice. Even though the recommendations in this memo focus primarily on actions at the state level, federal guidance on addressing rural invisibility in AI across health, education, and social systems can help create the conditions for states to act more effectively and equitably on behalf of rural communities.
The White House Office of Science and Technology Policy (OSTP) or the Domestic Policy Council (DPC) is well positioned to lead coordination across federal agencies, ensuring that rural AI implementation challenges are recognized in efforts affecting health, education, and social systems. Building on that coordination, the Office of Management and Budget (OMB) is well positioned to reinforce this work through its existing governance and procurement role to clarify how existing expectations for artificial intelligence procurement, validation, monitoring, oversight, and accountability apply in rural-serving settings. The Department of Health and Human Services (HHS), the Department of Agriculture (USDA), and the Department of Education (ED) should then help translate that guidance into practice for artificial intelligence systems and programs that directly affect rural communities. The National Institute of Standards and Technology (NIST) should provide supplemental examples showing how artificial intelligence risks can present differently in rural settings. This would strengthen implementation under existing frameworks without requiring the development of a separate framework.
Federal agencies should use existing programs to strengthen rural data infrastructure, technical assistance, and workforce readiness, and governance capacity needed for responsible AI implementation in rural communities. HHS, USDA, and ED can support rural-serving institutions directly, while NIST and other federal partners can provide tools, guidance and practical examples to help organizations implement AI responsibly and effectively.
The Need for Rural Proofing
Rural proofing is the process of systematically checking whether policies, tools, and investments reflect rural realities, avoid unintended rural harms, and support fair outcomes for rural communities. In practice, it means asking early and explicitly how a policy or AI system will function in places with lower population density, greater distance from services, thinner infrastructure, smaller administrative capacity, and different patterns of need and service use.
When applied to AI, rural proofing makes rural conditions visible across system design, data, deployment, and oversight. This includes defining clear use cases, keeping communities involved in decisions about AI, explaining what the system does and does not do, and regularly reviewing whether it creates unequal results. It also means regularly reviewing system performance, checking for weak results in small or low-volume populations, documenting when rural data is limited, and being transparent about how those limitations affect outcomes. Rather than treating rural impact as an afterthought, rural-proofing makes rural context and rural strengths a core part of design, implementation, oversight, and evaluation. Within governance processes, it also helps ensure that policies and decisions are informed by rural needs, contexts, and strengths rather than assumptions developed elsewhere.
Because many rural systems operate with limited staff, tight budgets, and shared regional responsibilities, AI governance requirements must be practical. Federal and state agencies should give rural-serving organizations the time, funding, and support needed to review systems, raise concerns, and participate in oversight. They should also provide plain-language documentation so local leaders, frontline staff, and community members can understand how decisions are being made. Finally, rural proofing requires clear accountability. When AI systems cause harm or fail to work fairly in rural communities, agencies and vendors should have a clear process to identify the problem, respond to it, and fix it (see Figure 1).
Plan of Action
Addressing rural invisibility in AI algorithms and systems across health and social sectors requires coordinated national attention and action, including the integration of rural proofing into national AI governance efforts. Because national frameworks often serve as guidance for states, progress at the national level is needed to provide the standards, expectations, and resources that support states in adapting AI governance to their specific contexts.In the meantime, states can begin building their own pathways by aligning with existing frameworks, piloting approaches in priority areas, and strengthening internal capacity.
Kansas as a Blueprint
As one of the nation’s rural states, Kansas has a strong interest in ensuring that AI systems work effectively for rural communities. As AI becomes increasingly integrated into sectors that are important to rural Kansan, including health care, education, transportation, agriculture, emergency response, public benefits, and other public services, rural-proofing can help ensure that AI tools are responsive to rural contexts.
For Kansas, this could include leveraging existing rural health infrastructure, engaging local stakeholders, and testing practical approaches that can be scaled as clearer national direction emerges. The Center for Medicare and Medicaid Services (CMS)’s Rural Health Transformation Program offers one practical pathway for aligning rural technology investment and technical assistance in Kansas with rural AI proofing principles. The Kansas Legislative Artificial Intelligence Task Force should explicitly include rural context as a defined part of its charge, membership, and workplan. The Kansas Office of Information Technology Services (OITS), the Information Technology Executive Council (ITEC), the Kansas Department of Health and Environment (KDHE), the Kansas Department for Aging and Disability Services (KDADS), and the Kansas Department for Children and Families (DCF) should work collectively to translate broad AI governance principles into practical oversight and implementation for rural health and social systems.
Furthermore, implementation of these recommendations can be staged based on current capacity, allowing agencies to begin with foundational actions and progressively build toward a more coordinated, statewide approach over time (see Figure 2).
Recommendation 1. Ensure The Kansas Legislative Artificial Intelligence Task Force and Any Future State-Level Task Forces Explicitly Include a Focus on Rural Context and Health
The Kansas Legislative Artificial Intelligence Task Force, given its role in shaping AI policy and direction, should explicitly include rural context as a defined part of its charge, membership, and workplan. The current task force already includes legislators, executive branch leadership, universities, health systems, agriculture, and private sector technology members. The taskforce’s scope could include reviewing AI use in rural contexts, incorporating rural and frontline voices into decisions around AI procurement and deployment, and issuing guidance on procurement, oversight, and accountability in rural health and social systems.
In practice, Kansas can build on the existing role of OITS by extending its coordination function to include AI-specific responsibilities, such as setting standards for evaluation, interoperability, and responsible use across agencies. ITEC can provide statewide governance direction by aligning AI efforts with broader IT strategy and policy. Service agencies, including KDHE, KDADS, and DCF would implement these efforts within health and social systems. This structure gives Kansas a practical model that other states can adapt by pairing a statewide IT authority with the agencies that directly manage public benefits, care, and social services.
- Define Scope. State-level Kansas AI task forces, working groups, and advisory bodies should explicitly include AI use and governance in rural contexts as a core scope of work, embedding rural considerations directly into the charge, membership, and workplan rather than treating them as secondary or optional.
- Coordinate Governance. Cross-agency coordination should occur through OTIS with statewide governance direction set through ITEC, while agencies including KDHE, KDADS, and DCF should identify and document where AI affects health and social service access.
- Establish Structure. Kansas should establish a clear governance structure that translates national AI principles into practical oversight, procurement, and implementation decisions tailored to rural conditions.
Recommendation 2. Require Rural Proofing for AI Used in Kansas Health and Social Service Programs
AI-enabled tools are expanding across eligibility decisions, care coordination, analytics, and service delivery. Because of this, Kansas should strengthen AI governance within the agencies that directly shape health and social outcomes. In practice, this work should begin with KDHE, KDADS, and DCF with cross-agency coordination support from OITS. Rather than relying only on broad fairness principles, these agencies should use a practical rural-proofing process to assess whether AI tools work reliably in rural settings with different staffing levels, service access, broadband conditions, data volume, and administrative capacity. Taking these steps now would help Kansas clarify oversight responsibilities, procurement standards, and rural risk before AI becomes more deeply embedded in public systems.
- Inventory. KDHE, KDADS, and DCF should identify and document current and planned uses of automated decision tools and AI systems used in decision-making that affect eligibility, benefits, care access, case management, service coordination, navigation, and enforcement. Agencies should require vendors to inventory AI used in care management, prior authorization, utilization management, member outreach, and provider network decisions. Kansas can then expand this inventory approach to other agencies that shape health, including housing, transportation, workforce, education, justice, and environmental systems.
- Rural AI Proofing. Agencies should apply rural-proofing review before they procure, renew, expand, modify, or deploy high-impact AI systems. This review should assess whether the tool performs reliably in rural settings, whether rural data are sufficient for validation, whether lower service use is being misread as lower need, and whether the system creates added burdens in places with limited staff, broadband, transportation, or service infrastructure.
Governance and Coordination. Kansas should establish a centralized cross-agency approach to AI governance to ensure consistency and avoid fragmented implementation across agencies. A coordinated structure—led jointly by OITS and state procurement—should define statewide oversight, reporting expectations, and minimum standards for the use of AI and automated decision tools. - Strengthen and Formalize AI Governance. Within this structure, agencies should strengthen and formalize AI governance by assigning clear oversight responsibility, documenting risk management and vendor review practices, incorporating transparency and explainability requirements, and building internal and community-level AI literacy. Agencies should also implement consistent oversight and reporting expectations for vendor AI use, including requirements for rural proofing, audit, and review mechanisms. OITS, in coordination with state procurement, should provide cross-agency technical guidance, ensure consistency in procurement standards, and align AI use with state IT and data governance policies. Individual agencies should retain program-level oversight within their statutory authority while operating within this coordinated governance framework.
- Vendor Requirements. Require vendors to explain how their AI tools perform in rural settings, disclose known data and performance limits, identify human review points, and provide plain-language documentation on system purpose, intended use, and potential failure points.
Recommendation 3. Institutionalize Rural Listening through Trusted Intermediaries
Meaningful engagement with rural communities is especially important in this context because AI systems are often designed and evaluated far from the places where their effects will be felt. However, engagement alone is insufficient. This recommendation draws a deliberate distinction between consultation, where agencies ask communities what they think, and co-governance, where rural communities hold real influence over AI decisions that affect them. Kansas should aim for co-governance, not just input collection In rural areas, where access to care, public services, transportation, broadband, and legal support may already be limited, even small design flaws or inaccurate assumptions can have outsized consequences. Regular listening with rural residents and trusted local partners can help surface needs, barriers, and unintended harms that may otherwise remain invisible in statewide decision-making.
- Support. OTIS or the Governor’s Office should coordinate recurring AI listening sessions, with participation from KDHE, KDADS and DCF, and with research and facilitation support provided by Kansas public universities.
- Leverage Partners. OITS and KDHE, KDADS, DCF should engage Kansas public universities to support session design, facilitation, documentation, synthesis of findings, and evaluation to ensure structured feedback and continuity across sessions.
- Conduct Sessions. KDHE, KDADS, DCF in partnership with county and local public health agencies, behavioral health providers, university extension networks, libraries, legal aid and court self-help programs, and community-based organizations, should conduct sessions using a shared calendar and unified feedback process.
- Apply Findings. KDHE, KDADS, DCF should use listening sessions as a rural-proofing mechanism to assess how AI-enabled tools in health, benefits, and social service programs affect access to care, legal protections, and social determinants of health in rural settings.
- Integrate Oversight. OITS and KDHE, KDADS, DCF should integrate findings into state oversight by documenting recurring rural issues, flagging systems for review, and using insights to strengthen procurement, monitoring, and accountability for AI systems used in Kansas programs.
Recommendation 4. Establish a Kansas ‘Rural AI Health Governance Blueprint’ for Other Rural States to Replicate
Clear leadership at the state level matters because rural proofing is unlikely to be applied consistently if agencies and vendors are left to interpret it on their own. A statewide approach creates shared expectations, strengthens accountability, and makes clear that rural context should be built into procurement, oversight, and evaluation from the beginning. This approach is also replicable because it relies on documented processes, practical tools, review steps, and implementation lessons that other rural states can adapt to fit their own governance structures, service systems, and community conditions. The framework should also incorporate AI infrastructure impacts, including data center siting, to ensure rural-proofing standards address the distribution of resource, environmental, and land use burdens associated with AI development.
- Document Framework. OITS, in coordination with the Information, ITEC and participating agencies such as KDHE, KDADS, and DCF, should document Kansas’s rural AI governance framework in a public implementation guide, including rural-proofing standards, shared workflows, listening session models, and transparency practices.
- Assess and Evaluate. KDHE, KDADS, DCF should partner with Kansas public universities to assess outcomes, identify lessons learned, and produce evidence-based recommendations to strengthen rural AI governance over time.
- Share and Scale. OITS and participating agencies should share implementation tools, templates, rural-proofing checklists, and model policies through interstate networks such as the National Governors Association, the National Conference of State Legislatures, and state rural health associations so other states can adapt the Kansas model.
- Pilot Collaboration. OITS, with support from Kansas public universities and partner agencies, should pilot cross-state collaboration with at least two predominantly rural states to test whether the Kansas workflow can transfer across different governance structures, agency arrangements, and service systems.
- Demonstrate Practice. OITS, state service agencies, and university partners should position Kansas as a practical demonstration state by showing how rural proofing can translate broad AI governance expectations into workable state practice that reflects rural conditions, administrative limits, and community realities.
Recommendation 5. Establish a Standardized and Contextualized Kansas Rural AI Health Literacy Framework
Kansas should complement the upstream AI governance framework with a statewide Rural Health AI Literacy Framework to ensure residents, students, and frontline workers can engage AI systems critically. Unlike general AI literacy, which often focuses on basic awareness of AI tools and digital skills, rural health AI literacy should prepare residents, students, frontline workers, and public institutions to understand how AI can shape health access, eligibility, referrals, triage, service coordination, and related decisions in rural communities. Governance structures alone are insufficient if communities lack shared standards for understanding how AI affects eligibility, health access, agriculture, transportation, and legal services in rural settings. The Kansas State Department of Education (KSDE), in coordination with the Kansas Board of Regents (KBOR) and the Kansas Office of Information Technology Services (OITS), should lead development of tiered, age-appropriate AI literacy competencies spanning K–12, postsecondary education, and public-sector roles.
To operationalize this framework, Kansas should:
- Integrate Curriculum. KSDE should integrate AI literacy into digital literacy, civics, agricultural education, and career and technical education standards, with support from Regional Education Service Centers for teacher training.
- Embed in Higher Education. KBOR should embed AI literacy modules into general education requirements and first-year seminars across public universities and community colleges, aligning with Higher Learning Commission expectations.
- Establish Rural Hubs. Kansas State University and Cooperative Extension should serve as rural AI literacy hubs delivering applied programming for health, agriculture, and local government sectors.
- Expand Community Delivery. State agencies and education partners should collaborate with public libraries, Tribal education departments, workforce development boards, and community-based organizations to deliver multilingual AI literacy programming statewide.
- Train Public Workforce. State agencies should implement baseline AI literacy training for employees in health, eligibility, and human service roles.
Conclusion
As AI becomes more embedded in public systems affecting health and social outcomes, it is important to account for rural context, particularly in Kansas, where many communities operate under conditions that differ from those assumed in typical AI development and deployment environments. These conditions include greater data sparsity, lower service density, and constrained institutional capacity for oversight. The proposed recommendations aim to operationalize responsible AI principles through coordinated cross-agency governance, integration of rural proofing into existing structures, and stronger community engagement in AI decision-making. By acting now, Kansas can build a more accountable model for rural AI governance and offer other rural states a practical path forward.
Rural health refers to the health outcomes, service access, and community conditions that shape well-being in rural communities. It includes access to healthcare, behavioral health, substance use treatment, prevention, workforce capacity, transportation, and the social determinants of health that affect whether rural residents can receive timely and appropriate care.
Common federal rural definitions include those developed by the U.S. Census Bureau, the Office of Management and Budget, and the U.S. Department of Agriculture Economic Research Service. The ideas, challenges and recommendations presented here within, but are not limited by, common rural definitions used across public health and health care. While rurality exists on a spectrum, definitions often use some combination of population thresholds, population density, housing density, and proximity to dense urban areas to define levels of rurality and urbanicity.
They should establish AI governance structures and policies, inventory current and planned AI use, assess whether tools are necessary and can function effectively in rural settings, document rural data limitations and oversight responsibilities, require vendor disclosure, and provide plain-language information about how systems work and how human review and oversight are incorporated into decision-making.
AI vendors should explain how their systems perform in rural settings, disclose known data and performance limitations, identify human review points, and provide plain-language documentation on system purpose, intended use, and conditions under which performance may vary.
Listening sessions help state agencies hear directly from rural residents, frontline workers, and local organizations about how AI affects access to care, benefits, legal navigation, and other services in practice. The memo recommends using those findings to improve procurement, monitoring, and accountability.
Procurement is not merely an administrative function—it is how AI enters government and the first line of defense for responsible AI in the public sector.
Responsible AI starts with who is in the data, who is at the table, whose needs shape the outcome, and who is responsible when it falls short.
Investment should instead be directed at sectors where American technology and innovation exist but the infrastructure to commercialize them domestically does not—and where the national security case is clear.
As of March 2026, there were at least nine documented U.S. wrongful arrests tied to face recognition misidentification. Errors like these are as much human as machine.