Can Generative AI Uncover Hidden Patterns in Historical Domestic Traffic Ads Through Data Analysis? A ChatLoS-DTA Exploration

This paper presents ChatLoS-DTA, a custom Generative Pre-trained Transformer (GPT) model specifically developed for data analysis on the Domestic Traffic Ads (DTA) Legacy of Slavery dataset. The DTA dataset consists of numerous historical newspaper advertisements from 1824 to 1864 for buying and selling enslaved individuals across Maryland. This dataset, digitized and curated by the Maryland archives, offers valuable insights into patterns within the domestic slave trade. However, certain accessibility challenges exist for non-technical users, including the descendants of enslaved individuals or cultural researchers. ChatLoS-DTA, built on OpenAI’s ChatGPT-4 and Python libraries, was designed to allow such users to query the dataset using natural language without requiring any technical expertise. This paper discusses ChatLoS-DTA’s architecture, ethical framework, performance, and limitations, highlighting the model’s potential as a template for applying generative AI in cultural and historical research. Future work includes refining the tool’s accuracy to broaden dataset compatibility and further enhance ethical safeguards.