Focusing on predictive algorithms and AI technologies in law enforcement, this paper argues that a digital humanist inquiry of the historical development of law enforcement in the United States is necessary for identifying how emerging policing technologies perpetuates systemic harm against marginalized communities by design.

We investigate the effectiveness of LLMs in evaluating text similarity, a fundamental task in CH research. We study the similarity of German poems from different perspectives, such as content or form. Our results show that recent commercial models are comparable to or better than supervised models (zeroshot, chain of thought).

This study employs natural language processing and machine learning to analyze religious trajectories in Holocaust survivor testimonies. Utilizing large language models, we reveal patterns of evolution in beliefs and practices under extreme conditions, offering insights into thematic narrative development and demonstrating the transformative potential of computational methods in historical analysis.