Skip to content

Artificial Intelligence and Electoral Democracy in Nigeria: Political Participation, Information Integrity and The Future of Democratic Governance

Sep 2026 · International Journal of Scientific Research Studies · Vol 03 · 0 citations

TL;DR

Nigeria's response to AI in elections should combine innovation with provenance, independent verification, institutional forensic capacity, platform accountability, media and AI literacy, and rights-preserving regulation.

Abstract

Artificial intelligence (AI) is becoming increasingly consequential to electoral information environments, although its democratic effects cannot be understood simply by treating every digital electoral technology as AI. This article examines how AI and AI-enabled information practices may shape electoral democracy and political participation in Nigeria, with particular attention to the 2023 general election and the institutional environment developing toward the 2027 electoral cycle. The study uses qualitative documentary research and thematic content analysis of peer-reviewed scholarship, Nigerian electoral and legal documents, institutional reports, and documented fact-checking evidence. The analysis distinguishes conventional digital electoral technologies, algorithmic systems, AI, generative AI, and synthetic media in order to avoid conceptual conflation. The evidence indicates that AI creates opportunities for voter information, civic verification, accessibility, administrative support and large-scale analysis, but also introduces risks associated with synthetic political content, impersonation, targeted disinformation, algorithmic amplification, uncertainty about content authenticity and gendered political abuse. Evidence from the 2023 election is particularly instructive because it includes both confirmed manipulated political audio and disputed or non-AI-generated material that was publicly described as a deepfake. This distinction reveals a deeper democratic problem: the erosion of citizens' ability to determine what political information can be trusted. The article develops an AI–Information Integrity–Participation–Democratic Legitimacy framework, arguing that the effects of AI are mediated by institutional capacity, regulation, platform governance, digital literacy, verification capacity and political trust. The article concludes that Nigeria's response to AI in elections should combine innovation with provenance, independent verification, institutional forensic capacity, platform accountability, media and AI literacy, and rights-preserving regulation.

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.