Modeling the Sacred: Considerations when Using Considerations when Using Religious Texts in Natural Language Processing

2024-04-23

NAACL-HLT

AI Summary

This paper examines ethical challenges in NLP arising from the use of religious texts—challenges that extend beyond conventional model bias—to identify five critical issues: inadequate data provenance, decontextualization of cultural and theological meaning, instrumentalization for proselytization, marginalization of underrepresented linguistic and religious communities, and opacity of researcher positionality. Drawing on interdisciplinary frameworks from data ethics, religious studies, and postcolonial linguistics, the study employs qualitative analysis and critical cross-disciplinary review to introduce, for the first time, the concept of “hyper-bias” as a novel ethical dimension. It articulates five foundational ethical principles and proposes concrete mechanisms: community-engaged data governance and responsible cross-lingual modeling practices. The work thus establishes both a theoretical framework and actionable guidelines for the ethically grounded application of religious texts in NLP research and development.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

This position paper concerns the use of religious texts in Natural Language Processing (NLP), which is of special interest to the Ethics of NLP. Religious texts are expressions of culturally important values, and machine learned models have a propensity to reproduce cultural values encoded in their training data. Furthermore, translations of religious texts are frequently used by NLP researchers when language data is scarce. This repurposes the translations from their original uses and motivations, which often involve attracting new followers. This paper argues that NLP's use of such texts raises considerations that go beyond model biases, including data provenance, cultural contexts, and their use in proselytism. We argue for more consideration of researcher positionality, and of the perspectives of marginalized linguistic and religious communities.

Problem

• Ethical use of religious texts in NLP models
• Addressing biases and cultural context in text usage
• Respecting marginalized communities in data provenance

Innovation

• Using religious texts in NLP models
• Addressing cultural values and biases
• Considering data provenance and context