Digital Manipulation of Democracy:
Abstract
The increasing use of artificial intelligence (AI) in political communication has drastically altered the dynamics of democratic politics. Initially, the digital technologies promised enhanced civilian engagement but later facilitated sophisticated forms of psychological manipulation through algorithmic targeting, behavioral profiling, and synthetic media. This study examines how AI-driven systems enable covert influence over voter behavior, thereby undermining epistemic autonomy and electoral integrity. Drawing on a qualitative multiple-case study approach, the study analyses key instances such as the Cambridge Analytica scandal and Russian information operations. It develops an integrated theoretical framework combining psychopolitics, nudge theory, and algorithmic governance to explain how digital infrastructures operationalise psychological control at scale. The findings demonstrate that AI transforms political communication from public persuasion to personalised behavioural engineering, fragmenting the public sphere, and intensifying polarization. The study contributes to the literature by conceptualizing AI enabled electoral manipulation as a form of psychological warfare and proposes a multi-level framework for democratic resilience, including regulatory reform, technological redesign, and civic empowerment. The study concludes by emphasizing the need to safeguard cognitive liberty as a core democratic value in the age of algorithmic power.
Read the paper
Related studies
- The neurocircuitry of addiction: an overview
- Gender Differences in Human Brain: A Review
- Stratified medicine for mental disorders
- The neurobiology of drug addiction: cross-species insights into the dysfunction and recovery of the prefrontal cortex
- Annual Research Review: DNA methylation as a mediator in the association between risk exposure and child and adolescent psychopathology
- Schizophrenia: The new etiological synthesis
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.