3.2. Foundations of AI Ethics

Artificial intelligence is not ethically neutral. From how training data is collected, to how outputs are used, AI systems reflect human priorities, assumptions, and blind spots. Before we can implement policies or design classroom guidelines, we need a strong foundation: what does it mean to use AI responsibly, and what values should guide us? This chapter introduces the core ethical principles that appear across global frameworks, explores their trade-offs, and situates them within higher education contexts.

Core Ethical Principles

Across dozens of international reports and guidelines, five principles consistently emerge:

• Fairness: AI should not reinforce inequities. Biased data and algorithms can deepen discrimination along lines of race, gender, or socioeconomic status.
• Accountability: AI may support decisions, but responsibility remains with humans. A system cannot carry moral or legal liability.
• Transparency: Users deserve to know how outputs are generated. Transparency builds trust and allows oversight.
• Privacy: Respect for personal data is essential. Collecting, storing, or sharing information without safeguards undermines autonomy.
• Human agency: People must remain “in the loop.” AI should extend human judgment, not override it.

Global Convergence

Despite cultural and political differences, a remarkable consensus has emerged worldwide:

• • UNESCO Recommendation on the Ethics of AI: centers human dignity, diversity, and sustainability.
  • OECD Principles on AI: emphasize transparency, human-centered values, robustness, and accountability.
  • European Union AI Guidelines: highlight fairness, prevention of harm, and explainability.
  • U.S. AI Bill of Rights and NIST AI Risk Management Framework: focus on equity, accountability, privacy, and protection of civil rights.
  • Jobin, Ienca, & Vayena (2019): survey of global principles showing convergence around transparency, fairness, responsibility, and privacy.

Tensions and Trade-offs

Ethical principles rarely align neatly. They often create tensions that require careful judgment:

• Transparency vs. privacy: Full transparency may require exposing personal or sensitive data.
• Fairness vs. efficiency: Slowing down a system to check for bias may reduce speed but increase justice.
• Human agency vs. automation: Keeping a person in the loop can safeguard judgment, but it may also reduce efficiency and scalability.

Why It Matters in Higher Education

For faculty, staff, and students, these principles are not abstract. They play out daily:

• Fairness: Admissions algorithms must avoid disadvantaging applicants from underrepresented groups.
• Accountability: Faculty remain responsible for grades, even if AI helps evaluate writing or problem sets.
• Transparency: Students deserve clarity about when AI is being used in classrooms and how it shapes their learning experience.
• Privacy: Using AI writing detectors raises concerns about data collection and surveillance.
• Human agency: Students must learn to use AI tools critically, not be replaced by them.

📚 Weekly Reflection Journal

Quick Self-Check

Decide whether each statement about AI ethics is true or false.

Looking Ahead

Now that we have outlined the foundations of AI ethics, the next chapter turns to implementation: how institutions and professionals put these principles into practice through policies, guidelines, and case studies.