AI-generated text detectors gain adoption in educational and professional contexts, their fairness remains underexamined. While prior research has uncovered isolated cases of bias, particularly against English Language Learners (ELLs), there is a lack of systematic evaluation of such systems across broader sociolinguistic factors. In this work, we propose a comprehensive evaluation framework for AI detectors across various types of biases. As part of this framework, we introduce a suite of targeted datasets spanning 7 major categories: demographics, age, educational grade level, dialect, formality, political leaning, and topic. Using this, we evaluate four open-source state-of-theart AI text detectors and find consistent disparities in detection performance, particularly low recall rates for texts from underrepresented groups. Our contributions provide a scalable, transparent approach for auditing AI detectors and emphasize the need for bias-aware evaluation before these tools are deployed for public use.

Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benchmarking. In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years’ NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach: 1) Use slide titles to retrieve relevant and engaging text, figures, and tables; 2) Summarize the retrieved context into bullet points with long-form question answering. Our evaluation suggests that long-form QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.