The development of ethical AI systems is currently geared toward setting
objective functions that align with human objectives. However, finding such
functions remains a research challenge, while in RL, setting rewards by hand is
a fairly standard approach. We present a methodology for dynamic value
alignment, where the values that are to be aligned with are dynamically
changing, using a multiple-objective approach. We apply this approach to extend
Deep $Q$-Learning to accommodate multiple objectives and evaluate this method
on a simplified two-leg intersection controlled by a switching agent.Our
approach dynamically accommodates the preferences of drivers on the system and
achieves better overall performance across three metrics (speeds, stops, and
waits) while integrating objectives that have competing or conflicting actions.

通过动态价值对齐的多目标方法，扩展了深度 Q-Learning 以适应多个目标，在一个由切换代理程序控制的简化交叉口上，动态适应驾驶员对系统的偏好，实现了在速度、停车和等待等三个指标上更好的综合性能，同时整合了具有竞争或冲突行为的目标。

通过多目标偏好聚合的动态价值调整

Dynamic value alignment through preference aggregation of multiple  objectives

Making moral judgments is an essential step toward developing ethical AI
systems. Prevalent approaches are mostly implemented in a bottom-up manner,
which uses a large set of annotated data to train models based on crowd-sourced
opinions about morality. These approaches have been criticized for potentially
overgeneralizing a limited group of annotators' moral stances and lacking
explainability. In contrast, top-down approaches make moral judgments grounded
in a set of principles. However, it remains conceptual due to the incapability
of previous language models and the unsolved debate among moral principles. In
this study, we propose a flexible framework to steer Large Language Models
(LLMs) to perform moral reasoning with well-established moral theories from
interdisciplinary research. The theory-guided top-down framework can
incorporate various moral theories. Our experiments demonstrate the
effectiveness of the proposed framework on datasets derived from moral
theories. Furthermore, we show the alignment between different moral theories
and existing morality datasets. Our analysis exhibits the potentials and flaws
in existing resources (models and datasets) in developing explainable moral
judgment-making systems.

提出了一个灵活的框架，利用跨学科研究中建立的道德理论，引导大型语言模型进行道德推理，证明了该框架在从道德理论中衍生的数据集上的有效性，展示了不同道德理论与现有道德数据集之间的一致性，并展示了开发可解释的道德判断系统中现有资源（模型和数据集）的潜力和缺陷。