This research challenges the idea that AI systems can be politically neutral, arguing that true neutrality is unattainable but can be approximated through careful design.

• Political bias in AI influences user opinions and decision-making
• Neutrality is subjective and compromised by biases in training data, algorithms, and user interactions
• Approximating neutrality requires transparent design choices, diverse perspectives, and context-aware systems
• Security implications include reduced manipulation risks and more accountable AI development

For security professionals, this research provides a framework to address political bias in AI systems, helping to mitigate information manipulation and develop more transparent, fair AI implementations.

Political Neutrality in AI is Impossible- But Here is How to Approximate it