Historical Forensic Auditing with Aligned and Non-Aligned AI

The paper proposes an innovative methodology for historical forensic auditing, leveraging the interaction between both aligned and non-aligned artificial intelligence (AI) engines. The goal is to overcome the limitations of traditional historical analysis, addressing issues such as bias, accuracy, and interpretation. The research explores how AI can authenticate digitized documents and analyze vast archival collections, while improving the detection and mitigation of historical, sampling, and cultural biases. The effectiveness of multi-agent systems in historical verification and practical applications, such as automated fact-checking, is examined, with a case study of the Apollo moon landing demonstrating how this interaction can lead to a more complete understanding of past events. Finally, the paper discusses the technical, ethical, and methodological challenges and future directions for integrating AI into historiography.