Prior work has explicated the coloniality of artificial intelligence (AI) development and deployment through mechanisms such as extractivism, automation, sociological essentialism, surveillance, and containment. However, that work has not engaged much with alignment: teaching behaviors to a large language model (LLM) in line with desired values, and has not considered a mechanism that arises within that process: moral absolutism -- a part of the coloniality of knowledge. Colonialism has a history of altering the beliefs and values of colonized peoples; in this paper, I argue that this history is recapitulated in current LLM alignment practices and technologies. Furthermore, I suggest that AI alignment be decolonialized using three forms of openness: openness of models, openness to society, and openness to excluded knowledges. This suggested approach to decolonial AI alignment uses ideas from the argumentative moral philosophical tradition of Hinduism, which has been described as an open-source religion. One concept used is vi\'{s}e\d{s}a-dharma, or particular context-specific notions of right and wrong. At the end of the paper, I provide a suggested reference architecture to work toward the proposed framework.

翻译：先前的研究已通过提取主义、自动化、社会学本质主义、监控与管控等机制，阐释了人工智能（AI）开发与部署中的殖民性。然而，这些研究较少涉及对齐问题——即向大语言模型（LLM）教授符合预期价值观的行为，且未考虑该过程中产生的一种机制：道德绝对主义——这是知识殖民性的组成部分。殖民主义在历史上曾改变被殖民民族的信仰与价值观；本文主张，当前大语言模型对齐实践与技术正在重演这段历史。此外，我建议通过三种形式的开放性去殖民化人工智能对齐：模型的开放性、面向社会的开放性，以及面向被排除知识的开放性。这种去殖民化人工智能对齐的提议方法借鉴了印度教论证性道德哲学传统中的思想——该传统曾被描述为一种开源宗教。其中一个使用的概念是Viśeṣa-Dharma，即特定情境下的对错观念。在论文结尾，我提供了一个建议性的参考架构，以推动实现所提出的框架。