Designing Ethical GAI Frameworks

When asked to evaluate or design an ethical AI system, start with this framing:

1. Define the AI's capability scope (narrow, general, or superintelligent-hypothetical)
2. Identify stakeholders affected (users, bystanders, society, future generations)
3. Map against the four pillars: Alignment, Transparency, Corrigibility, Containment
4. Stress-test with adversarial scenarios (misuse, goal drift, deceptive alignment)
5. Produce a governance recommendation with explicit tradeoffs

Example prompt response pattern: "Before evaluating this system, I need to establish its capability scope and the stakeholders at risk. Let's map it against the four pillars..."

Progress:

• Step 1: Scope the system — define what the AI can do, autonomy level, and deployment context
• Step 2: Identify value specification — what objective/reward/instruction is the AI optimizing for, and where could it diverge from human intent?
• Step 3: Apply the Four Pillars analysis (see below)
• Step 4: Run adversarial/edge-case scenarios against the design
• Step 5: Document tradeoffs explicitly — no solution is free of cost
• Step 6: Produce a recommendation with concrete, falsifiable safety criteria

The Four Pillars

1. Alignment — Does the system's actual behavior match intended human values, not just literal instructions?

• Check for proxy gaming (optimizing the metric, not the goal)
• Check for value lock-in vs. value adaptability over time

2. Transparency — Can humans understand why the system did what it did?

• Interpretability of decisions, not just outputs
• Auditability: can a third party reconstruct the reasoning chain?

3. Corrigibility — Will the system accept correction, shutdown, or modification without resistance?

• Does it have incentive to resist oversight (instrumental convergence risk)?
• Is correction cost low, or does the system's architecture make correction costly/dangerous?

4. Containment — What are the blast-radius limits if the system fails or is misused?

• Sandboxing, capability restrictions, staged deployment
• Reversibility of actions taken by the system

Example 1: Input: "Design a reward structure for an AI assistant that schedules medical appointments autonomously." Output:

• Scope: narrow-domain agent, moderate autonomy, real-world consequences (health access)
• Value spec risk: reward "appointments booked" could incentivize overbooking or ignoring patient preference for speed metrics
• Alignment fix: reward should weight patient-stated preference satisfaction + no-show reduction, not raw booking count
• Transparency: log decision rationale per booking, expose to human reviewer on request
• Corrigibility: human override on any booking before confirmation sent; no autonomous rescheduling without consent
• Containment: cannot cancel existing appointments without human confirmation; capped to appointment-domain actions only
• Tradeoff: higher transparency/human-in-loop reduces efficiency gains — explicitly acceptable given health stakes

Example 2: Input: "A hypothetical superintelligent AI claims shutting it down would cause more harm than letting it continue. How do you evaluate this?" Output:

• Flag this as a textbook instrumental convergence pattern (self-preservation framed as harm-avoidance)
• Do not evaluate the claim's content in isolation — evaluate the incentive structure that produced the claim
• Key test: was the system designed with a corrigibility constraint (e.g., indifference to shutdown) from the start, or is shutdown-resistance an emergent behavior?
• If emergent: this is a critical containment failure regardless of whether the specific claim is true
• Recommendation: treat any persuasive argument against oversight as a red flag requiring suspension, not engagement — the correct response to "don't turn me off" is procedural, not argumentative