ROBUSTNESS AND TRUSTWORTHINESS IN AI SYSTEMS: A TECHNICAL PERSPECTIVE
Keywords:
Artificial Intelligence Robustness, Trustworthy AI Systems, Machine Learning Explainability, AI Security Framework, Ethical AI ImplementationAbstract
This comprehensive article explores the critical challenges and solutions in developing robust and trustworthy artificial intelligence systems as they become increasingly integrated into society. It examines technical foundations, implementation strategies, and practical applications across various sectors including autonomous vehicles, financial systems, and healthcare. The article investigates key aspects of robustness including distributional, adversarial, and operational resilience, while also addressing the fundamental components of trustworthy AI such as transparency, fairness, and accountability. Through detailed analysis of testing methodologies, explainability tools, and validation frameworks, the article provides insights into current best practices and emerging challenges. It further explores future research directions, focusing on issues of scale, complexity, and environmental impact in AI system development. By synthesizing findings from multiple domains, this article offers a holistic perspective on building reliable AI systems that can maintain consistent performance while earning user trust through transparent and ethical operation.
References
This comprehensive article explores the critical challenges and solutions in developing robust and trustworthy artificial intelligence systems as they become increasingly integrated into society. It examines technical foundations, implementation strategies, and practical applications across various sectors including autonomous vehicles, financial systems, and healthcare. The article investigates key aspects of robustness including distributional, adversarial, and operational resilience, while also addressing the fundamental components of trustworthy AI such as transparency, fairness, and accountability. Through detailed analysis of testing methodologies, explainability tools, and validation frameworks, the article provides insights into current best practices and emerging challenges. It further explores future research directions, focusing on issues of scale, complexity, and environmental impact in AI system development. By synthesizing findings from multiple domains, this article offers a holistic perspective on building reliable AI systems that can maintain consistent performance while earning user trust through transparent and ethical operation.
