Moral Uncertainty
Summary
Moral uncertainty is a concept in AI alignment and ethics that acknowledges the existence of multiple plausible moral theories and the difficulty in determining which one is definitively correct. This approach recognizes that ethical decision-making should consider various moral frameworks rather than adhering strictly to a single theory. In the context of reinforcement learning and AI development, incorporating moral uncertainty can help create more ethically robust agents by preventing extreme behaviors that might result from commitment to a single moral theory. However, implementing moral uncertainty in AI systems presents technical challenges, such as determining how to balance and compare incompatible reward functions derived from different ethical frameworks. Research in this area aims to develop morally competent AI agents that can navigate complex ethical decisions while accounting for the inherent uncertainty in moral philosophy, potentially contributing to both practical AI applications and the computational grounding of ethics.