Prince, Enamul HoqueIslam, Mohammed Saidul2025-11-112025-11-112025-08-202025-11-11https://hdl.handle.net/10315/43335Data-driven storytelling is a powerful method for conveying insights by combining narrative techniques with visualizations and text. In this thesis, we introduce a novel task for data story generation and a benchmark containing 1,449 stories from diverse sources. We propose a multi-step LLM agent framework mimicking the human storytelling process: one for planning and narration, and another for verification at each intermediary step. Results show that our proposed framework significantly outperforms non-agentic baselines. In parallel, we recognize that trustworthy storytelling must also be fair and unbiased. To this end, we conduct a largescale empirical study to uncover systematic geo-economic bias in the foundational subtask of data storytelling: producing narrative summaries of charts. We further explore inference-time debiasing strategies and highlight the need for more robust bias mitigation methods. Together, these contributions provide both a powerful generative system and a fairness-focused evaluation to ensure automated data storytelling is accurate, coherent, and ethically responsible.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceElectronic Thesis or Dissertation2025-11-11Data storytellingBias evaluationLarge language modelsLLM agents