Skip to content

NAVIGATING ARTIFICIAL INTELLIGENCE AND RELIGIOUS AUTHORITY IN NIGERIA: RECONFIGURING RELIGIOUS KNOWLEDGE, LEADERSHIP AND TRUST IN THE DIGITAL AGE

Oct 2026 · British Journal of Advanced Research
Media, Religion, Digital Communication

Abstract

Artificial Intelligence (AI) is increasingly becoming part of religious education, communication, research and everyday faith practices. Believers can now use generative AI to explain sacred texts, compare interpretations, prepare devotional materials and seek answers to religious questions without first consulting a recognised religious leader. This development raises an important question for Nigeria: how is religious authority changing when access to religious information is no longer controlled mainly by clergy, scholars and religious institutions? This paper examines the relationship between AI and religious authority in Nigeria, focusing on religious knowledge, leadership and trust. Using a conceptual and interpretive methodology, it synthesises scholarship on religious authority, digital religion, AI ethics and Nigerian religious experience. It argues that AI is not simply replacing religious authorities; rather, it is reconfiguring the conditions under which authority is produced, accessed, evaluated and trusted. Nigerian digital-religion research shows that digital infrastructures can create new forms of religious influence and authority, while recent AI scholarship highlights concerns about accuracy, privacy, bias and human accountability. The paper proposes a Responsible Religious AI Engagement Framework based on human accountability, verification, contextual sensitivity, confidentiality and formation. It concludes that Nigerian religious institutions should neither reject AI nor surrender religious judgment to it. AI should remain an assistive instrument while theological discernment, pastoral responsibility, communal recognition and human dignity remain central to religious leadership.

Read PDF

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9
#artificial intelligence Review Oct 2025

Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition

This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...

Ranjan Sapkota, Manoj Karkee · 112 citations · ⚡10

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.