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AI ethics isn't just a philosophical sidebar—it's the framework that determines whether AI systems help or harm society, and it's increasingly central to technology policy exams. You're being tested on your ability to connect abstract principles like transparency, accountability, and fairness to concrete policy mechanisms and real-world consequences. Understanding these guidelines means understanding how governments, companies, and international bodies attempt to govern technologies that can influence everything from hiring decisions to criminal sentencing.
These principles don't exist in isolation. They overlap, sometimes tension with each other, and require trade-offs that policymakers must navigate. When you study these guidelines, don't just memorize definitions—know which principles address user rights, which focus on system performance, and which govern organizational responsibility. That's what separates a surface-level answer from one that demonstrates genuine policy thinking.
These principles center on what individuals can expect and demand from AI systems that affect their lives. The core mechanism here is informed consent and meaningful choice.
Compare: Transparency vs. Privacy—both are user-focused rights, but they can conflict when explaining a decision requires revealing data about other users. If an FRQ asks about trade-offs in AI governance, this tension is your best example.
These guidelines address how AI systems should function technically. The underlying principle is that ethical AI must be reliable enough to trust with consequential decisions.
Compare: Robustness vs. Safety—robustness focuses on consistent performance under normal variation, while safety addresses harm prevention under adversarial or extreme conditions. Both matter, but safety carries legal liability implications.
These principles define who is responsible when AI systems cause harm. The mechanism is establishing clear chains of responsibility and remediation pathways.
Compare: Accountability vs. Human Oversight—accountability addresses after-the-fact responsibility, while oversight focuses on ongoing control. Policy frameworks increasingly require both: humans in the loop and clear liability when things go wrong.
These guidelines address AI's effects on society and specific groups. The core principle is that technological efficiency cannot justify discriminatory outcomes.
Compare: Fairness vs. Inclusivity—fairness focuses on preventing harm to specific groups, while inclusivity focuses on extending benefits to underserved populations. Both address equity, but from opposite directions.
These principles address how AI should be developed and managed over time. The mechanism is embedding ethics into organizational workflows, not treating it as an afterthought.
Compare: Ethical Design Processes vs. Accountability—design processes are preventive (building ethics in), while accountability is reactive (assigning responsibility when things fail). Strong governance requires both upstream and downstream mechanisms.
| Concept | Best Examples |
|---|---|
| User Rights | Transparency, Privacy, Inclusivity |
| System Performance | Robustness, Safety and Security |
| Organizational Responsibility | Accountability, Human Oversight |
| Social Impact | Fairness, Environmental Sustainability |
| Governance Mechanisms | Ethical Design Processes, Stakeholder Engagement |
| Preventing Harm | Fairness, Safety, Bias Auditing |
| Enabling Benefits | Inclusivity, Accessibility, Transparency |
| Legal Compliance Focus | Privacy (GDPR), Accountability (EU AI Act), Fairness (Civil Rights Law) |
Which two principles both address user empowerment but can come into tension when explaining AI decisions? What policy mechanisms attempt to balance them?
If a hiring algorithm produces equitable outcomes on average but disadvantages candidates with disabilities, which principles are implicated and how do they differ in their approach?
Compare and contrast robustness and safety: How would you explain to a policymaker why both are necessary, and what different risks does each address?
An FRQ asks you to evaluate a company's AI governance framework. Which principles would you check for preventive measures versus reactive measures, and why does the distinction matter?
Environmental sustainability seems disconnected from other AI ethics principles. Identify one principle it directly relates to and explain the connection in terms of resource allocation and social impact.