Unpacking the Ethical Value Alignment in Big Models
- URL: http://arxiv.org/abs/2310.17551v1
- Date: Thu, 26 Oct 2023 16:45:40 GMT
- Title: Unpacking the Ethical Value Alignment in Big Models
- Authors: Xiaoyuan Yi, Jing Yao, Xiting Wang and Xing Xie
- Abstract summary: This paper provides an overview of the risks and challenges associated with big models, surveys existing AI ethics guidelines, and examines the ethical implications arising from the limitations of these models.
We introduce a novel conceptual paradigm for aligning the ethical values of big models and discuss promising research directions for alignment criteria, evaluation, and method.
- Score: 46.560886177083084
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Big models have greatly advanced AI's ability to understand, generate, and
manipulate information and content, enabling numerous applications. However, as
these models become increasingly integrated into everyday life, their inherent
ethical values and potential biases pose unforeseen risks to society. This
paper provides an overview of the risks and challenges associated with big
models, surveys existing AI ethics guidelines, and examines the ethical
implications arising from the limitations of these models. Taking a normative
ethics perspective, we propose a reassessment of recent normative guidelines,
highlighting the importance of collaborative efforts in academia to establish a
unified and universal AI ethics framework. Furthermore, we investigate the
moral inclinations of current mainstream LLMs using the Moral Foundation
theory, analyze existing alignment algorithms, and outline the unique
challenges encountered in aligning ethical values within them. To address these
challenges, we introduce a novel conceptual paradigm for aligning the ethical
values of big models and discuss promising research directions for alignment
criteria, evaluation, and method, representing an initial step towards the
interdisciplinary construction of the ethically aligned AI
This paper is a modified English version of our Chinese paper
https://crad.ict.ac.cn/cn/article/doi/10.7544/issn1000-1239.202330553, intended
to help non-Chinese native speakers better understand our work.
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