AI Control Through A Suite of Detection and Correction Mechanisms

Abstract: This doctoral dissertation investigates critical aspects of AI safety, particularly in the domains of adversarial robustness and safety monitoring, areas that often receive less attention despite the significant advances in AI and Machine Learning (ML) performance. Current solutions to address AI vulnerabilities are typically too complex and difficult to implement in real-world scenarios, particularly when complete datasets are unavailable, computational resources are limited, or the nature of threats remains ambiguous. This research aims to develop advanced detection mechanisms that can identify vulnerabilities at both data and model levels, alongside adaptive correction methods designed to efficiently mitigate these risks. The ultimate goal is to enhance the security, reliability, and resilience of AI systems, with a focus on practical applications adaptable to real-world constraints.

Introduction: As AI and ML technologies continue to evolve, their impact across diverse fields, including computer vision and natural language processing, has been substantial. However, while AI systems excel in these domains, their safety and robustness against adversarial threats have been largely overlooked. The ability to detect and respond to vulnerabilities in AI models, especially in constrained environments, is paramount to ensuring long-term reliability and trust in these systems. This dissertation seeks to address these gaps by developing a robust framework that incorporates advanced detection mechanisms and adaptive correction methods, specifically designed to operate effectively under real-world conditions.

Objectives:

• Investigate existing AI safety solutions with a focus on adversarial robustness and vulnerability detection.
• Develop detection mechanisms capable of identifying vulnerabilities at both data and model levels.
• Design adaptive correction methods to mitigate risks efficiently in constrained computational environments.
• Test the framework’s efficacy in enhancing AI system resilience in various real-world scenarios with limited datasets and computational resources.
• Provide recommendations for integrating these methods into practical AI systems across different domains.

Methodology: The methodology is divided into several key phases:

• Literature review: Conduct a thorough analysis of current AI safety measures, adversarial robustness research, and vulnerability detection techniques.
• Framework design: Develop a comprehensive framework consisting of advanced detection mechanisms and adaptive correction methods.
• Testing and validation: Implement and test the framework in simulated environments to evaluate its effectiveness in identifying and mitigating AI vulnerabilities.
• Real-world scenario analysis: Apply the framework in practical, real-world scenarios to test its adaptability and performance in constrained environments.
• Evaluation: Assess the framework’s impact on AI system security, reliability, and resilience, comparing it against existing solutions.

Expected Contributions:

• A novel suite of detection mechanisms capable of identifying AI vulnerabilities at both data and model levels.
• Development of adaptive correction methods that efficiently mitigate AI system risks in real-world scenarios with limited resources.
• Enhancement of AI system security and robustness through the proposed framework.
• Practical guidelines for the implementation of the framework in diverse applications, addressing real-world constraints and resource limitations.

Conclusion: This dissertation contributes to the field of AI safety by proposing practical and adaptable solutions to the challenges of adversarial robustness and safety monitoring. Through the development of advanced detection mechanisms and adaptive correction methods, the research enhances the security, reliability, and resilience of AI systems. These solutions are designed to be implementable in real-world scenarios, providing a path forward for safer AI systems in various applications.