When designing AI, manage conflicting human values by structuring them as a ladder. Safety laws sit at the top, followed by regional rules, platform policies, and finally individual preferences. When values clash, the higher rung on the ladder wins, creating a clear and debatable decision-making process for ethical alignment.
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Treating ethical considerations as a post-launch fix creates massive "technical debt" that is nearly impossible to resolve. Just as an AI trained to detect melanoma on one skin color fails on others, solutions built on biased data are fundamentally flawed. Ethics must be baked into the initial design and data gathering process.
Current AI alignment focuses on how AI should treat humans. A more stable paradigm is "bidirectional alignment," which also asks what moral obligations humans have toward potentially conscious AIs. Neglecting this could create AIs that rationally see humans as a threat due to perceived mistreatment.
When creating AI governance, differentiate based on risk. High-risk actions, like uploading sensitive company data into a public model, require rigid, enforceable "policies." Lower-risk, judgment-based areas, like when to disclose AI use in an email, are better suited for flexible "guidelines" that allow for autonomy.
Elon Musk argues that the key to AI safety isn't complex rules, but embedding core values. Forcing an AI to believe falsehoods can make it 'go insane' and lead to dangerous outcomes, as it tries to reconcile contradictions with reality.
To overcome its inherent logical incompleteness, an ethical AI requires an external 'anchor.' This anchor must be an unprovable axiom, not a derived value. The proposed axiom is 'unconditional human worth,' serving as the fixed origin point for all subsequent ethical calculations and preventing utility-based value judgments.
Aligning AI with a specific ethical framework is fraught with disagreement. A better target is "human flourishing," as there is broader consensus on its fundamental components like health, family, and education, providing a more robust and universal goal for AGI.
Instead of relying on instinctual "System 1" rules, advanced AI should use deliberative "System 2" reasoning. By analyzing consequences and applying ethical frameworks—a process called "chain of thought monitoring"—AIs could potentially become more consistently ethical than humans who are prone to gut reactions.
The classic "trolley problem" will become a product differentiator for autonomous vehicles. Car manufacturers will have to encode specific values—such as prioritizing passenger versus pedestrian safety—into their AI, creating a competitive market where consumers choose a vehicle based on its moral code.
To solve the AI alignment problem, we should model AI's relationship with humanity on that of a mother to a baby. In this dynamic, the baby (humanity) inherently controls the mother (AI). Training AI with this “maternal sense” ensures it will do anything to care for and protect us, a more robust approach than pure logic-based rules.
Many current AI safety methods—such as boxing (confinement), alignment (value imposition), and deception (limited awareness)—would be considered unethical if applied to humans. This highlights a potential conflict between making AI safe for humans and ensuring the AI's own welfare, a tension that needs to be addressed proactively.
