Fake News Detector: Ethical, Psychological, and Societal Dimensions of AI-Based Misinformation Detection

The exponential growth of misinformation has disrupted democratic discourse, public health communication, and social cohesion. AI-based tools have emerged as essential mechanisms for identifying and mitigating fake news. Situated within broader social, ethical, and psychological contexts, this paper presents the Fake News Detector, a machine learning-based text classification system. While leveraging natural language processing and supervised learning algorithms, this study underlines human cognitive biases, ethical dilemmas in algorithmic decision-making, and societal consequences of automated truth arbitration. Irrespective of technical performance, responsible AI deployment calls for fairness, interpretability, transparency, and media literacy-oriented education. The Fake News Detector serves both as a technical prototype and a philosophical case study in human-machine collaboration against information disorder.