Humanistic and Historical Inquiry in the Age of AI: Challenges, Methods, and Futures

This paper contributes to ongoing conversations within the humanities, particularly in the field of history, regarding the challenges and opportunities posed by artificial intelligence (AI). It begins by examining public and scholarly responses to generative AI, noting shared patterns of concern rooted in ethical, professional, and societal considerations. The paper then revisits the prevailing humanistic approaches that focus on AI’s limitations and suggests complementing them using the bias-variance framework that treats AI as a technologically complex, socially embedded, and historically evolving system. By providing a concise history of machine learning and natural language processing, the paper situates contemporary AI within a longer historical and technical lineage. It further explores the parallels between AI modeling and historical reasoning, particularly their shared inferential logic, and highlights recent interdisciplinary studies that leverage computation to uncover historical and social patterns. The paper concludes by advocating for a more holistic approach to AI, one that bridges technical understanding with historical inquiry to examine how AI systems construct meaning, reshape knowledge production, and influence global socio-political dynamics.