What is the ethical and responsible use of AI in higher education?

I came across this concept of FATE: Fairness, Accountability, Transparency, and Ethics from Microsoft and other organizations. The question above, my curiosity about ethics frameworks, and the FATE concept led me to think more about the ethical and responsible use of AI in higher education.

The appropriate use of Generative AI like ChatGPT in the classroom dominates discussions on social media. But, applied to all facets within higher education institutions, how do campus community members and external stakeholders practice the principles of ethical and responsible use of AI?

This blog post is a high-level overview of this topic. I prompted ChatGPT with a series of prompts, and the majority of the content of this post is generated by ChatGPT.

The ethical and responsible use of Artificial Intelligence (AI) in higher education involves navigating a complex landscape of considerations to ensure that the deployment of these technologies is done in a manner that upholds the fundamental principles of fairness, transparency, and accountability, among others. The discussions around this topic can be enriched by understanding various perspectives and guidelines highlighted in academia and the broader educational sector.

Ethics Frameworks:

Ethics, a branch of philosophy dealing with what is morally right or wrong, has various frameworks that provide structured approaches to evaluating moral issues. These frameworks offer perspectives based on principles, values, or virtues, guiding individuals or organizations in ethical decision-making ​1​. Here are some frameworks and a brief description of each:

  • Virtue Ethics: Focuses on the inherent character of a person rather than on specific actions. It emphasizes virtues or moral character.
  •  Deontology: Based on rules and duties. It suggests an action is morally right if it follows a set rule or duty, regardless of the outcome.
  •  Rights and Responsibilities: Emphasizes the rights of individuals and the responsibilities that come with these rights.
  •  Consequentialism: Suggests that the morality of an action is judged solely by its consequences.
  •  Care-based Ethics: Stresses the importance of healthy relationships and the well-being of individuals and their interdependence ​2​.
  •  ETHICS Model: A theoretically grounded ethical decision-making model encompassing utilitarianism, moral relativism, and moral egoism as part of its framework ​3​.
  •  Framework for Ethical Decision Making (Markkula Center): A framework designed as an introduction to thinking ethically, aiding individuals in reflecting on the ethical aspects of their decisions ​4​.
  •  Framework for Making Ethical Decisions (Ethics at Work): Provides a summary of the major sources for ethical thinking and presents a framework for decision-making while considering multiple perspectives, ethical theories, and principles ​5​.

Ethical and Responsible Use of AI in Higher Education:

Understanding AI and its Implications:

It’s crucial for stakeholders in higher education, including educators, administrators, and leaders, to develop a foundational understanding of AI technologies. This understanding should extend to the design, implementation, and implications of AI in educational settings. A well-informed stakeholder community can engage in meaningful dialogue and decision-making processes concerning the ethical deployment of AI in higher education​1​.

Engaging stakeholders in higher education through workshops on AI technologies, offering courses on AI ethics, organizing public lectures by AI experts, developing online resources explaining AI, and establishing forums for open discussions help foster a foundational understanding of AI and its implications in educational settings.

Ethical Design, Implementation, and Governance:

Ethical considerations should permeate the design, implementation, and governance of AI systems. These considerations include leveraging existing legal and regulatory frameworks and developing new governance structures to ensure fairness, transparency, and accountability in AI applications within higher education ​1​.

Establishing ethics committees, developing ethical guidelines for AI research, conducting regular audits to ensure compliance with ethical and legal standards, incorporating mechanisms for accountability and redress, and engaging with external ethics boards form a framework for the ethical design, implementation, and governance of AI in higher education.

Addressing Equity and Discrimination:

AI presents unique challenges to equity in education, especially concerning commonly held views on discrimination. It’s vital to raise awareness about these challenges and work towards mitigating biases and promoting educational fairness ​1​.

Implementing bias detection and mitigation tools in AI systems, conducting regular training on equity and inclusivity, establishing channels for reporting AI-related discrimination, partnering with diverse communities, and collaborating with experts on equity are crucial steps toward addressing the challenges of equity and discrimination posed by AI.

Human Rights Perspective:

Ethically deploying AI in higher education also involves adopting a human rights perspective, ensuring that the technology does not infringe upon the rights of individuals but rather supports equitable access to educational resources and opportunities ​1​.

Ensuring adherence to privacy laws, conducting human rights impact assessments, establishing a human rights-centered framework for AI governance, collaborating with human rights organizations, and promoting transparency and informed consent are measures to align AI deployment with a human rights perspective in higher education.

Bias Mitigation and Privacy Protection:

The ethical aspect of AI use in higher education significantly revolves around addressing AI error, bias, discrimination, and data privacy. It’s essential to work towards reducing biases and protecting individuals’ privacy ​1​.

Implementing strict data anonymization and encryption standards, deploying explainable and auditable AI systems, establishing a robust data governance framework, conducting regular privacy audits, and engaging in ongoing dialogue with stakeholders is pivotal in mitigating biases and protecting privacy.

Human Responsibility:

Ethical AI underscores human responsibility in creating and deploying AI systems. The human element ensures that AI systems operate within ethical boundaries​ 2​.

Establishing clear lines of accountability, providing training for human overseers of AI systems, ensuring human-in-the-loop mechanisms in critical decision-making processes, encouraging ethical leadership, and promoting a culture of responsibility are steps to underscore human responsibility in the deployment and management of AI systems.

Promoting Digital Literacy:

Ensuring that educators and students have the necessary digital literacy to understand, interact with, and critically evaluate AI technologies is an integral part of ethical AI use in higher education ​1​.

Offering digital literacy courses, developing resources on digital rights and online safety, organizing digital literacy campaigns, establishing partnerships with tech companies for training, and creating platforms for sharing digital literacy resources are measures to foster digital literacy among educators and students.

Interdisciplinary Collaboration:

Addressing the ethical implications of AI in higher education necessitates interdisciplinary collaboration, drawing insights from ethics, law, social sciences, and other fields​ 3​.

Forming interdisciplinary committees, organizing cross-departmental workshops, encouraging joint research projects on AI ethics, establishing partnerships with other institutions, and promoting dialogues between academia, industry, and government are initiatives to foster interdisciplinary collaboration on ethical AI in higher education.

Promoting Ethical Practices in Classrooms:

Encouraging, practicing, and supporting the ethical and responsible use of AI in classrooms is pivotal, requiring multiple ways to think through the use of generative AI in teaching and learning​ 4​.

Integrating AI ethics discussions within course curricula, encouraging faculty to adopt ethical AI tools, organizing classroom discussions on real-world AI ethical dilemmas, encouraging students to undertake projects exploring the ethical dimensions of AI, and providing resources for teachers to explore ethical AI applications help in promoting ethical practices in classrooms.


Exploring the ethical and responsible AI use in higher education requires examination of the topic from various disciplines. The concept of FATE—Fairness, Accountability, Transparency, and Ethics—guides this exploration. Ethics frameworks act as roadmaps in understanding this complex topic. Stakeholders representing diverse campus communities should be involved in designing and implementing AI policies and practices.