AI Ethics in Historical Research: A Framework for Bias Mitigation, Transparency, and Accountability

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into historical research offers unprecedented opportunities for analyzing vast and previously inaccessible datasets. However, this technological expansion also introduces significant ethical and epistemic challenges that threaten the principles of scholarly integrity. The increasing reliance on AI-driven tools risks reproducing structural biases embedded in historical data, undermining interpretive transparency through opaque algorithmic processes, and diffusing accountability across a complex chain of technological actors. This paper examines these challenges through three interdependent ethical dimensions-Bias, Transparency, and Accountability-and proposes an integrated framework for responsible AI use in historical scholarship. It argues that algorithmic systems trained on historically biased data risk perpetuating epistemic exclusion, while the opacity of deep learning models' conflicts with the historian's duty to justify interpretation and evidence. To address these issues, the framework combines Critical Archival Theory for understanding systemic bias, Explainable AI (XAI) for interpretability, and multi-stakeholder governance models for enforcing accountability across developers, institutions, and researchers. The findings underscore that the future of AI-augmented historical research depends on sustained human oversight, transparent computational methods, and the establishment of interdisciplinary ethical standards that safeguard both epistemic integrity and equitable representation in digital history.