SCHEDULE
Calibrated Information Extraction from Coastal Ecosystems Literature
by Kevin Quinn
A large portion of data for freshwater and coastal ecosystems exists within text, tables, and figures from PDF research papers. Generative AI is increasingly used as a tool for extracting such data, but is subject to high risk inaccuracies (e.g. 'hallucinations'). We propose to surmount this drawback through a novel technique: calibrated information extraction. We develop mechanistic interpretability tools for probing an LLM's internal activation patterns and producing confidence scores for extracted data points. In turn, we show that strong calibration among scores suggests a path for reliably supporting ecological research in downstream statistical models or analyses.
Stop the Nonconsensual Use of Nude Images in Research (Published at NeurIPS 2025 - Oral)
by Princessa Cintaqia
In order to train, test, and evaluate nudity detection models, machine learning researchers typically rely on nude images scraped from the Internet. Our research finds that this content is collected and, in some cases, subsequently distributed by researchers without consent, leading to potential misuse and exacerbating harm against the subjects depicted. We argue that the distribution of nonconsensually collected nude images by researchers perpetuates image-based sexual abuse and that the machine learning community should stop the nonconsensual use of nude images in research. To characterize the scope and nature of this problem, we conducted a systematic review of papers published in computing venues that collect and use nude images. Our results paint a grim reality: norms around the usage of nude images are sparse, leading to a litany of problematic practices like distributing and publishing nude images with uncensored faces, and intentionally collecting and sharing abusive content. We conclude with a call-to-action for publishing venues and a vision for research in nudity detection that balances user agency with concrete research objectives. You can check out the paper here: openreview.net/pdf?id=Ev5xwr3vWh