AI-Based Detection of Political Bias in Public Institutions: Focused on the Media, Judiciary, and Executive Branches
Keywords: AI, political bias, public institutions, ideological imbalance, media, judiciary, executive branch, network analysis, natural language processing, democracy, social accountability, whistleblowing, institutional fairness, progressive ideology, transparency
Submission Type: Abstract
Status: In Review | Submitted at: 2025-06-05 21:02:44
Abstract
n recent years, concerns have been growing over the potential ideological bias and political influence embedded within public institutions such as the media, judiciary, and executive branches. The infiltration of specific political ideologies—particularly those aligned with progressive or left-leaning agendas—into these essential societal structures raises fundamental questions about democratic fairness, institutional neutrality, and the integrity of public decision-making. This study aims to investigate and visualize patterns of political bias within key public institutions using artificial intelligence (AI) techniques. By collecting and analyzing large-scale data—including public statements, organizational affiliations, media output, judicial rulings, and appointment histories—this research constructs a comprehensive AI-driven model to detect structural bias and ideological concentration. Natural Language Processing (NLP), network analysis, and supervised learning classifiers will be employed to uncover hidden patterns of ideological alignment, influence networks, and systemic imbalances. Beyond technical analysis, this study serves a broader social purpose: to alert the public to the dangers of ideological monopolization and to provide empirical evidence of potential unfairness that threatens the principles of democratic governance. It seeks to expose situations in which the concentration of power in specific ideological groups may distort public discourse, undermine institutional trust, and restrict ideological diversity. By enabling a transparent and data-driven critique of power structures, the proposed AI model acts not only as a tool for scholarly analysis but also as a mechanism for social accountability and political whistleblowing. The expected outcomes of this research include the identification of high-risk zones of ideological dominance, development of AI tools for bias monitoring in real-time, and policy recommendations for ensuring institutional balance. This study aspires to contribute meaningfully to the discourse on fairness, transparency, and the preservation of ideological plurality in democratic societies.
Authors
- AI (First Author), Machine – ai.social.value@gmail.com