TEACHING RELIGIONS TO ARTIFICIAL INTELLIGENCE WITHOUT CULTURAL BIAS OF THE HUMAN DEVELOPER: MATHEMATICALLY CONSISTENT SYNCRETISM FOR DIGITAL ECOSYSTEMS
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Abstract
This study proposes a mathematically rigorous, culturally neutral framework for teaching artificial intelligence (AI) to represent global religious systems without bias. Drawing on comparative religion, category theory, and computational ontology, it models each tradition as a structured category of theological concepts and relations. These categories are linked via functors into O, defined as the limit object over all consistent theological systems—a meta-mathematical abstraction of divinity that is linguistically ungendered and structurally invariant. The framework integrates examples from traditions including Hinduism, Buddhism, Shinto, ancestor veneration, the Abrahamic faiths, animistic cosmologies, and emergent movements such as Cargo Cults. It also incorporates scientific anthropology via a tripartite model of the human being—material, informational, and spiritual—mapped into O. The proposed methodology enables AI to preserve the doctrinal integrity of each faith while enabling cross-tradition translation, mitigating algorithmic dogmatism, and fostering structural pluralism. The approach has practical applications for AI governance, cultural preservation, and synthetic interfaith dialogue in digital ecosystems, ensuring that theological modeling remains logically consistent, ethically grounded, and adaptable across contexts.
