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This paper critiques traditional AI alignment methods like RLHF and Constitutional AI, arguing they are too rigid and disconnected from real-world impacts. It advocates for a pragmatic alignment strategy that prioritizes empirical evidence and the observable impacts of AI systems. The paper proposes reversing the logic of alignment, deriving principles from observed outcomes rather than pre-defined ethical rules, to ensure AI development aligns with societal values.
Forget top-down ethics: AI alignment should be driven by the real-world impacts of AI systems, not pre-defined principles.
Abstract The alignment of artificial intelligence (AI) systems with societal values and the public interest is a critical challenge in the field of AI ethics and governance. Traditional approaches, such as Reinforcement Learning with Human Feedback (RLHF) and Constitutional AI, often rely on pre-defined high-level ethical principles. This article critiques these conventional alignment frameworks through the philosophical perspectives of pragmatism and public interest theory, arguing against their rigidity and disconnect with practical impacts. It proposes an alternative alignment strategy that reverses the traditional logic, focusing on empirical evidence and the real-world effects of AI systems. By emphasizing practical outcomes and continuous adaptation, this pragmatic approach aims to ensure that AI technologies are developed according to the principles that are derived from the observable impacts produced by technology applications.