Combining Robustness and Explainability in Developing Safe Artificial Intelligence Systems

Abstract

This study investigates the critical challenges associated with ensuring the security and robustness of artificial intelligence (AI) systems, especially within high-stakes applications such as autonomous vehicles, healthcare, and financial technologies. The primary objective is to identify vulnerabilities in AI algorithms and propose effective mitigation strategies. The research emphasizes contemporary threats, including adversarial attacks, algorithmic opacity, data breaches, and the ethical ramifications of AI deployment. A review of current literature reveals that adversarial attacks, where subtle input perturbations cause significant misclassifications, present a considerable risk to AI reliability. Techniques such as robust training, involving training models on adversarial examples, have shown effectiveness in improving resilience, albeit with higher computational demands. The study also explores the importance of explainable AI (XAI) tools like LIME and SHAP, which enhance transparency by clarifying the decision-making processes of complex models. This transparency is vital for fostering user trust, especially in fields like medicine and finance, where understanding AI decisions is essential. XAI approaches enable better oversight and adherence to ethical standards. Data privacy concerns are addressed through methods such as differential privacy, which protects sensitive information by adding noise, and federated learning, which enables decentralized model training without exposing raw data. The findings indicate that these strategies secure data while maintaining model efficacy. By integrating robustness and explainability, this study contributes practical solutions to strengthen AI systems against evolving threats, advancing AI security and fostering trust in these technologies.