Given the success of ChatGPT, LaMDA and other large language models (LLMs),
there has been an increase in development and usage of LLMs within the
technology sector and other sectors. While the level in which LLMs has not
reached a level where it has surpassed human intelligence, there will be a time
when it will. Such LLMs can be referred to as advanced LLMs. Currently, there
are limited usage of ethical artificial intelligence (AI) principles and
guidelines addressing advanced LLMs due to the fact that we have not reached
that point yet. However, this is a problem as once we do reach that point, we
will not be adequately prepared to deal with the aftermath of it in an ethical
and optimal way, which will lead to undesired and unexpected consequences. This
paper addresses this issue by discussing what ethical AI principles and
guidelines can be used to address highly advanced LLMs.

通过讨论伦理人工智能原则和指南的使用，本文旨在解决高级大型语言模型（LLMs）的伦理问题。

高级大语言模型的治理和利用的道德人工智能原则和指南

Ethical Artificial Intelligence Principles and Guidelines for the  Governance and Utilization of Highly Advanced Large Language Models

In the context of designing and implementing ethical Artificial Intelligence
(AI), varying perspectives exist regarding developing trustworthy AI for
autonomous cars. This study sheds light on the differences in perspectives and
provides recommendations to minimize such divergences. By exploring the diverse
viewpoints, we identify key factors contributing to the differences and propose
strategies to bridge the gaps. This study goes beyond the trolley problem to
visualize the complex challenges of trustworthy and ethical AI. Three pillars
of trustworthy AI have been defined: transparency, reliability, and safety.
This research contributes to the field of trustworthy AI for autonomous cars,
providing practical recommendations to enhance the development of AI systems
that prioritize both technological advancement and ethical principles.

这项研究探讨在设计和实施具有伦理问题的人工智能时，对于为自动驾驶汽车开发可信人工智能存在不同的观点，旨在通过探索多元观点，识别造成差异的关键因素，并提出弥合差距的策略。研究的三个支柱为透明度、可靠性和安全性，为自动驾驶汽车的可信人工智能领域做出了贡献，提供了实际建议来提高技术进步和道德原则的平衡发展。

设计师与工程师之间寻找差异，为自动驾驶汽车开发可信 AI

Finding differences in perspectives between designers and engineers to  develop trustworthy AI for autonomous cars

In this meta-ethnography, we explore three different angles of ethical
artificial intelligence (AI) design implementation including the philosophical
ethical viewpoint, the technical perspective, and framing through a political
lens. Our qualitative research includes a literature review that highlights the
cross-referencing of these angles by discussing the value and drawbacks of
contrastive top-down, bottom-up, and hybrid approaches previously published.
The novel contribution to this framework is the political angle, which
constitutes ethics in AI either being determined by corporations and
governments and imposed through policies or law (coming from the top), or
ethics being called for by the people (coming from the bottom), as well as
top-down, bottom-up, and hybrid technicalities of how AI is developed within a
moral construct and in consideration of its users, with expected and unexpected
consequences and long-term impact in the world. There is a focus on
reinforcement learning as an example of a bottom-up applied technical approach
and AI ethics principles as a practical top-down approach. This investigation
includes real-world case studies to impart a global perspective, as well as
philosophical debate on the ethics of AI and theoretical future thought
experimentation based on historical facts, current world circumstances, and
possible ensuing realities.

本研究通过哲学伦理观点、技术角度以及政治视角三个不同的角度探讨了伦理人工智能技术设计实施，旨在发掘伦理人工智能技术的长期影响和预期成果，在此基础上提出了一种新的政治角度的框架，探讨了其上下结合和混合技术的方式，最终总结了伦理人工智能技术发展的策略和理念。

人工智能道德的情境化：自上而下，自下而上和混合模型的理论与应用伦理学的元民族志

Contextualizing Artificially Intelligent Morality: A Meta-Ethnography of Top-Down, Bottom-Up, and Hybrid Models for Theoretical and Applied Ethics in Artificial Intelligence