Fake News Detectors: Implementation & Ethical Analysis

About the Assignment

The project explores the implementation and ethical implications of machine learning-based fake news detection using methods inspired by Advancing Fake News Detection: Hybrid Deep Learning With FastText and Explainable AI by Ehtesham Hashmi et al. The project demonstrates the feasibility of training machine learning models, such as Support Vector Machines (SVM) and FastText, for fake news detection and examines normative considerations like transparency, user privacy, and consent. The project required a rigorous technical analysis of the dataset, including data cleaning, feature extraction, and model evaluation. The final report includes a comprehensive overview of the methods used, ethical considerations, and recommendations for future research in this area. The project highlights the importance of ethical considerations in machine learning and the need for transparency and accountability in AI systems. The code is available on GitHub for further exploration and analysis.