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。

动态规范性：价值对齐的必要与充分条件

Dynamic Normativity: Necessary and Sufficient Conditions for Value  Alignment

The striking recent advances in eliciting seemingly meaningful language
behaviour from language-only machine learning models have only made more
apparent, through the surfacing of clear limitations, the need to go beyond the
language-only mode and to ground these models "in the world". Proposals for
doing so vary in the details, but what unites them is that the solution is
sought in the addition of non-linguistic data types such as images or video
streams, while largely keeping the mode of learning constant. I propose a
different, and more wide-ranging conception of how grounding should be
understood: What grounds language is its normative nature. There are standards
for doing things right, these standards are public and authoritative, while at
the same time acceptance of authority can and must be disputed and negotiated,
in interactions in which only bearers of normative status can rightfully
participate. What grounds language, then, is the determined use that language
users make of it, and what it is grounded in is the community of language
users. I sketch this idea, and draw some conclusions for work on computational
modelling of meaningful language use.

本文讨论了自然语言处理的困境，提出了一种新的理解语言与世界的整合方式 —— 以语言的规范性为中心，探讨了如何基于语言用户共同的认知、信任和行为规范，进行有意义的语言使用和计算建模。