The Dark Side of Artificial Intelligence: A Systematic Literature Review and Bibliometric Analysis
Abstract
Artificial intelligence is increasingly embedded in organizational decision-making, public administration, healthcare, marketing, education, scientific communication, and digital infrastructure. This expansion has produced a growing body of research on the dark side of AI, including ethical failure, accountability gaps, strategic risk, privacy loss, surveillance, misinformation, labour disruption, and environmental burden. This manuscript presents a systematic literature review supported by bibliometric analysis. The review is framed through PICO logic and reported using PRISMA 2020 principles. The Scopus dataset and associated evidence sources identified 86 records across the review corpus, screened 81 records after deduplication, considered 32 original Scopus papers as the main bibliometric evidence base, and included 19 studies in the core qualitative synthesis, with 15 supplementary sustainability and infrastructure records used as thematic extension evidence. The bibliometric analysis shows accelerated annual scientific production after 2022, dispersed source outlets, uneven country-level production and citation influence, fragmented collaboration structures, and a thematic map organized around artificial intelligence, risk management, risk assessment, governance, and emerging socio-technical concerns. The qualitative synthesis identifies eight domains of dark-side AI: sustainability and infrastructure burden; governance and accountability; ethics and human interests;…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.