The critical inquiry pervading the realm of Philosophy, and perhaps extending
its influence across all Humanities disciplines, revolves around the
intricacies of morality and normativity. Surprisingly, in recent years, this
thematic thread has woven its way into an unexpected domain, one not
conventionally associated with pondering "what ought to be": the field of
artificial intelligence (AI) research. Central to morality and AI, we find
"alignment", a problem related to the challenges of expressing human goals and
values in a manner that artificial systems can follow without leading to
unwanted adversarial effects. More explicitly and with our current paradigm of
AI development in mind, we can think of alignment as teaching human values to
non-anthropomorphic entities trained through opaque, gradient-based learning
techniques. This work addresses alignment as a technical-philosophical problem
that requires solid philosophical foundations and practical implementations
that bring normative theory to AI system development. To accomplish this, we
propose two sets of necessary and sufficient conditions that, we argue, should
be considered in any alignment process. While necessary conditions serve as
metaphysical and metaethical roots that pertain to the permissibility of
alignment, sufficient conditions establish a blueprint for aligning AI systems
under a learning-based paradigm. After laying such foundations, we present
implementations of this approach by using state-of-the-art techniques and
methods for aligning general-purpose language systems. We call this framework
Dynamic Normativity. Its central thesis is that any alignment process under a
learning paradigm that cannot fulfill its necessary and sufficient conditions
will fail in producing aligned systems.

本研究论文旨在探讨道德和人类价值观在人工智能研究中的重要性，并提出了一个名为 Dynamic Normativity 的框架来解决通过学习范式对人工智能系统进行对齐的问题。