Max Gonzalez Saez-Diez will present his MSE thesis "D3: Dataset, Diagnosis, and Deployment of Video Models for Police Body-Worn Camera Understanding" on Monday, April 20th, 2026 in CS 402 at 12:00pm. Advisor: Peter Henderson Reader: Olga Russakovsky All are welcome to attend. Abstract: Vision-language models are increasingly applied to police body-worn camera (BWC) video, yet few public benchmarks exist and researchers have little means to evaluate private vendor claims. This thesis addresses all three gaps: constructing the data necessary for training, evaluating models in a zero-shot manner on long-tailed phenomena, and demonstrating how this data can be used to train and deploy models in practice. First, we introduce EgoPolice. To the best of our knowledge, it is the largest existing dataset for egocentric understanding of real body-worn camera footage. Second, we present BWC-Bench, a zero-shot captioning benchmark that scores whether each listed action is asserted in the generated caption. Third, we describe work with a partner agency that provided access to their videos and use-of-force reports. To the best of our knowledge, this constitutes the first analysis of real police body-worn camera footage and associated use-of-force reports at this scale. A physical-interaction detector trained on EgoPolice ranks clips from a large private archive for human review. We find that models can support human-in-the-loop analysis at a scale not previously possible and uncover evidence of substantial underreporting of use-of-force events.