How University Leaders Can Ethically and Responsibly Implement AI

• By Bruce Dahlgren
• 02/27/25

For university leaders, the conversation around implementing artificial intelligence (AI) is shifting. With its great potential to unlock transformative innovation in education, it's no longer a question of if, but how, institutions should look to utilize the technology on their campuses.

AI is reshaping education, offering personalized learning, efficiency, and accessibility. For students, AI provides individualized support, and for faculty it streamlines administrative tasks. The promise of AI and its potential benefits for students, faculty, and higher education institutions at large is too great to pass up.

But every new and powerful technology comes with risk, and university leaders are right to be wary of the potential downfalls. Harmful bias, inaccurate output, lack of transparency and accountability, and AI that is not aligned to human values are just some of the risks that need to be managed to allow for the safe and responsible use of AI.

To fully leverage AI while mitigating these risks, university leaders must adopt a responsible and ethical approach — one that is proactive, thoughtful, and grounded in a framework of trust. This requires not just a structured implementation plan but also a strong foundation of guiding principles that inform every stage of the process.

The Principles of Trustworthy AI

Responsible AI implementation in higher education must be built upon a set of core principles that guide decision-making, policies, and deployment. These principles, aligned with frameworks such as the NIST AI Risk Management Framework, the EU AI Act, and the OECD AI Principles, establish the ethical and operational standards necessary for AI's successful integration.

• Fairness and Reliability: AI systems must be designed to minimize bias and ensure their outputs are consistent, valid, and equitable.
• Human Oversight and Value Alignment: AI should enhance, not replace, human decision-making — especially in matters with legal or ethical implications. Its design and use must align with the values of the students, faculty, and administrators engaging with it.
• Transparency and Explainability: Users should always know when AI is being used, understand how it works, and be able to interpret its outputs accurately.
• Privacy, Security, and Safety: AI systems must be designed to protect user data, ensure security, and minimize risks that could compromise institutional or personal safety.
• Accountability: Institutions and AI providers must establish clear accountability structures, ensuring responsible AI use and ethical oversight.

These principles do not represent a single step in the process; rather, they underpin every action taken to implement AI. They serve as the foundation for policy development, program design, and ongoing governance, ensuring AI is integrated in a way that prioritizes ethical considerations and institutional integrity.

Creating Policies and Programs for Implementation

With those key principles top of mind, creating policies and programs that clearly define what AI implementation will look like is key to ensuring the most effective use of the technology within an institution. Key considerations when creating these policies and programs include:

• A range of diverse and cross-functional voices represented in the discussion: Institutions should make sure they include all stakeholders in the policy formation process, including student representation. While not all stakeholders will have equal input on the policy formation, and some may only need to be kept informed of the process, these stakeholders should include those likely to use or benefit from AI within an institution's ecosystem, as well as those who have a role to play in managing the risks of using AI.
• A defined institutional position on AI: Tailored to existing positions on AI within an institution and the general attitude towards the technology on campus, defining a broad culture around AI lays the groundwork for these policies and programs. Perhaps a culture of exploration and innovation is best, or conversely, the appropriate culture may be one of risk-reduction and control.
• Policies that address matters relevant to a given institution: Depending on their defined institutional position on AI, institutions should consider which facets of its operations should leverage AI. Options include governance, teaching and learning, operations and administration, copyright and intellectual property, research, and academic dishonesty.

Getting to a final version of a policy is an iterative process, and keeping the stakeholders engaged and providing feedback remains important at this stage. The final policy should strive to balance addressing potential risks with enabling innovation and experimentation without being overly prescriptive by using a risk-based approach.

Implementing Policies and Programs with Strong Processes and Responsibility

A well-defined policy is only the first step. Effective implementation requires clear processes, governance structures, and training programs. Institutions must designate responsible parties for overseeing AI initiatives and establish a structured rollout plan that includes:

1. A Defined Implementation Timeline: Institutions should determine when policies take effect, considering whether a phased approach is appropriate.  
2. Clear Communication Strategies: Messaging should be consistent, transparent, and tailored to different campus audiences. It's important to also consider the frequency of those messages.
3. Comprehensive Training Plans: Faculty, staff, and students must be equipped with the knowledge and skills to use AI tools effectively and responsibly. This ensures that the AI tools an institution leverages are being used to their fullest potential, in the right way. Institutions can leverage existing training tools and processes, leverage third-party provided content and develop training internally as they see fit.
4. Ongoing Monitoring & Compliance: Institutions need mechanisms to track AI adoption, address noncompliance, and refine policies as AI technology evolves. While this is especially important in the rollout phase, it should be an ongoing process to monitor AI adoption.

While institutions vary in their approach to AI adoption, not utilizing it strategically risks falling short in meeting the evolving needs of students, faculty, and administrators. Instead, by following key principles, establishing comprehensive policies and programs, and implementing them with clear processes and accountability, institutions can responsibly and ethically harness the benefits of this transformative technology.