The Rise of AI Audit Trails

Abstract

This chapter considers the novel notion of an AI audit trail which is significant for building traceability in a range of AI industries. AI is being adopted fully in sectors like healthcare, finance, and manufacturing, and companies would require 'sophisticated systems' to be able to trace audit 'records' and meet regulations, ethical responsibility, and be operationally transparent. This chapter is an evolution 'low-tech' manual log systems to high-tech systems trace architectures that are able to 'capture' model logic, data ‘provenance', user contact, and context of decisions via AI, machine learning, and natural language processing. This chapter starts from the fundamentals and moves onto the significant techno-economic layers like machine learning and anomaly detection for practical value and transactional compliance and risk containment through transparency. Application is demonstrated in the reviews of implemented case studies in finance, healthcare, and government services while coping with the challenges of scale, privacy, and regulations. Ethical considerations open the review. From the black box opacity of AI systems, the authors propose a glass box trust auditing. This is the direction of policy and foundational AI research and systems with auditability, stability, and transparency.

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