--- title: "AI Governance Framework" slug: "appendix-c-ai-governance-framework" kind: "appendix" order: 25 label: "Appendix C" letter: "C" summary: "Along with new capabilities in the university, AI also introduces new responsibilities. Every decision concerning AI is ultimately a decision about institutional trust. Students trust that the" words: 1481 --- Along with new capabilities in the university, AI also introduces new responsibilities. Every decision concerning AI is ultimately a decision about institutional trust. Students trust that the university will protect their privacy and evaluate them fairly. Faculty trust that academic freedom will be respected and that scholarly work will not be reduced to automated processes. Researchers trust that new technologies will strengthen rather than compromise the integrity of scientific inquiry. Staff trust that innovation will improve their ability to serve the institution rather than simply increase efficiency at the expense of professional judgment. These responsibilities cannot be fulfilled through technology alone. Nor can they be addressed solely through policy. Governance exists because neither technical controls nor written rules are sufficient by themselves. A university requires a system of leadership, accountability, oversight, and continuous evaluation that ensures artificial intelligence remains aligned with institutional mission as technologies, regulations, and educational practices continue to evolve. Universities have always been places where new ideas are explored, tested, challenged, and refined. Excessively restrictive governance can become just as harmful as inadequate governance because it prevents institutions from learning through responsible experimentation. Effective governance therefore creates the conditions under which innovation can flourish safely, transparently, and responsibly. This framework is designed for colleges, universities, and systems of higher education regardless of institutional size or mission. Research universities, liberal arts colleges, community colleges, faith-based institutions, technical colleges, and online universities will inevitably implement AI differently. The principles presented here are intended to provide a common foundation while allowing each institution to develop governance structures appropriate to its own culture, regulatory environment, and strategic priorities. ## The Purpose of AI Governance Research involving human participants requires oversight. Financial operations follow established controls. Academic programs undergo review and accreditation. Information security is governed through formal policies and technical safeguards. These governance mechanisms provide consistency, accountability, and public confidence. Artificial intelligence should be viewed in the same way. AI systems increasingly influence teaching, advising, admissions, research, administrative services, communications, and institutional planning. As their influence grows, universities must ensure that decisions involving AI remain understandable, accountable, and consistent with institutional values. Artificial intelligence may inform decisions, recommend actions, or automate routine processes, but institutional accountability never transfers from human leadership to software. ## Principles of Responsible AI Governance Individual policies will change as technology evolves, but foundational principles should remain stable enough to guide future decisions that cannot yet be anticipated. ### Mission Before Technology Artificial intelligence should be adopted when it advances the educational, scholarly, research, or service mission of the university. Every significant implementation should begin by identifying the educational or operational objective being served and demonstrating how AI contributes meaningfully toward that objective. ### Human Accountability Faculty remain responsible for academic standards. Researchers remain responsible for scholarly integrity. Administrators remain responsible for operational decisions. Executive leaders remain responsible for institutional strategy. AI may assist these responsibilities, but it does not assume them. ### Transparency Transparency does not require revealing proprietary algorithms or technical implementation details. It requires communicating clearly where AI is being used, what purpose it serves, what information it accesses, and how human oversight is maintained. ### Fairness Universities should evaluate AI systems for unintended bias, unequal treatment, and disparate impact wherever institutional decisions may affect individuals or groups differently. Fairness requires continuous monitoring because institutional data, student populations, and external technologies change over time. A system that performs appropriately today may require adjustment in the future. ### Privacy Information should be collected, accessed, retained, and shared only to the extent necessary for legitimate educational or operational purposes. Privacy protections should be integrated into system design rather than added after implementation. ### Security Identity management, access controls, encryption, monitoring, incident response, and technical resilience remain essential regardless of how advanced AI capabilities become. Intelligent systems should operate within the same security expectations that govern other institutional technologies. ### Continuous Learning Institutions should evaluate outcomes, learn from implementation, revise policies when necessary, and document lessons that strengthen future decision-making. Governance succeeds when the university becomes progressively wiser rather than progressively more restrictive. ## Institutional Governance Structure Responsibility should never be concentrated entirely within information technology, academic affairs, legal counsel, or executive leadership. The governing board establishes overall institutional expectations concerning risk, accountability, and strategic direction. The president provides executive leadership and ensures that AI initiatives remain aligned with institutional mission. The provost safeguards academic quality, faculty participation, curriculum, research, and educational integrity. The chief information officer develops the technical architecture required to support secure and interoperable AI systems. General counsel advises on legal obligations, while privacy, cybersecurity, accessibility, compliance, and institutional research contribute specialized expertise within their respective domains. Decisions affecting teaching, curriculum, assessment, research practice, and academic standards should reflect meaningful faculty participation. Shared governance has long been one of higher education's defining characteristics, and responsible AI implementation should strengthen rather than bypass that tradition. Students should also participate in governance whenever AI materially affects their educational experience. Their perspectives often reveal practical concerns that institutional leaders may overlook. The following structure illustrates a typical governance model. |**Governance Body**|**Primary Responsibility**| |---|---| |Governing Board|Strategic oversight and institutional accountability| |President and Executive Cabinet|Executive sponsorship, mission alignment, resource allocation| |AI Governance Committee|Institution-wide policy, prioritization, oversight, and review| |Academic Affairs|Teaching, curriculum, assessment, faculty engagement| |Information Technology|Architecture, security, identity, interoperability, operations| |Research Leadership|Scholarly use, research integrity, compliance| |Legal, Privacy, and Compliance|Regulatory guidance, contracts, privacy, risk management| |Faculty Governance|Academic policy, curriculum, instructional standards| |Student Representation|Student experience, transparency, feedback, trust| ## Roles and Responsibilities Governance becomes effective only when responsibilities are clearly understood. Ambiguity often produces duplicated effort, delayed decisions, inconsistent implementation, or uncertainty about accountability. Every institution should therefore define which groups approve initiatives, which groups provide advice, which groups implement decisions, and which groups evaluate outcomes. Executive leadership is responsible for institutional direction. Faculty determine academic matters. Technology leaders ensure secure and reliable implementation. Operational leaders redesign workflows within their areas of responsibility. Legal, privacy, accessibility, and compliance professionals review institutional obligations. Internal audit or equivalent oversight functions evaluate whether governance processes are operating as intended. Faculty should disclose expectations for AI use within their courses. Staff should follow approved procedures when using institutional AI services. Researchers should apply scholarly standards consistently regardless of whether AI contributes to aspects of the research process. Students should understand institutional expectations concerning transparency, attribution, and responsible use. Governance therefore exists at multiple levels simultaneously. Every member of the university community participates in sustaining responsible institutional practice. ## Risk Classification Framework Not every use of artificial intelligence presents the same level of institutional risk. Asking an AI system to summarize a publicly available report differs substantially from using AI to support admissions review, evaluate student performance, recommend financial aid interventions, or process confidential health information. Governance should therefore distinguish among use cases according to their potential impact rather than applying identical review requirements to every implementation. Institutions should classify AI applications according to both the sensitivity of the information involved and the consequences of incorrect recommendations or decisions. Higher-risk applications require stronger governance, more extensive testing, greater transparency, and closer human oversight. Lower-risk applications may proceed through simplified review processes that encourage innovation without unnecessary administrative burden. A practical classification model may resemble the following: ### Typical Examples ### Governance Expectations ### Risk ### Level > **Low** Drafting communications, summarizing public documents, brainstorming ideas > **Low** Drafting communications, Departmental approval and basic summarizing public documents, acceptable-use policies brainstorming ideas > **Moderate** Student tutoring, faculty support, Privacy review, technical administrative workflow assistance evaluation, documented oversight > **High** Admissions support, advising Formal governance review, recommendations, financial aid human approval, continuous guidance, student success monitoring interventions > **Critical** Decisions involving legal rights, Executive oversight, employment actions, disciplinary comprehensive review, outcomes, protected research data, documented accountability, health or safety mandatory human decision authority Risk classification should remain dynamic. A system initially introduced for low-risk purposes may require additional governance if its responsibilities expand over time. Institutions should therefore review classifications regularly rather than assuming they remain appropriate indefinitely. ## Human Oversight Human oversight requires that individuals possess sufficient knowledge, authority, information, and time to exercise genuine professional judgment. It preserves the central principle that universities remain communities of human judgment, even as they increasingly collaborate with intelligent systems. Faculty should be able to reject AI-generated instructional recommendations when they conflict with disciplinary standards. Advisors should understand why an intervention has been suggested before acting upon it. Admissions professionals should review recommendations within the broader context of institutional policy and applicant circumstances. Researchers should remain accountable for scholarly conclusions regardless of the analytical tools employed during the research process. Meaningful oversight also requires escalation pathways. Every significant AI-supported service should include mechanisms through which students, employees, or external stakeholders can request review by an appropriately qualified person whenever automated assistance proves inadequate, incorrect, or inappropriate. Trust depends not only upon intelligent systems functioning well but also upon institutions responding responsibly when those systems fail.