Vision language models (VLMs) demonstrate strong zero-shot performance, but often perpetuate social stereotypes in person-centric queries, yielding skewed demographic distributions.Current debiasing methods apply uniform bias corrections across all input queries regardless of their bias sensitivity, creating a fundamental fairness-utility trade-off.Strong debiasing distorts semantically meaningful information in bias-insensitive queries, while weak debiasing fails to mitigate stereotypes in biassensitive ones.This one-size-fits-all approach hampers simultaneously achieving high utility on bias-insensitive queries and fairness on bias-sensitive queries.We introduce Reward-Gated Test-Time Adaptation (RG-TTA), a reinforcement learning-based test-time adaptation framework that selectively applies debiasing based on input sensitivity.RG-TTA adaptively triggers fairness regularization based on the bias sensitivity of each input during testtime policy adaptation, while focusing exclusively on optimizing cross-modal alignment for bias-insensitive inputs.Experiments on fairness benchmarks (e.g., FairFace, UTKFace) demonstrate substantial bias reduction while simultaneously improving zero-shot utility, resolving the trade-off of uniform debiasing.

This study examines the impact of the 'diversity' of product recommendations on the 'preference' of a customer, using online/offline data from a leading fashion company. First, through interviews with fashion professionals, we categorized the characteristics of customers into four types - gift, coordinator, carry-over, and trendsetter. Then, using a hybrid filtering method, we increased the accuracy and diversity of recommended products. We derived 13 salient features that reflect customer behavior based on the Purchase Funnel model and built a classification model that predicts a customer's preference rates. Second, we conducted two large-scale user tests with 20,000 real customers to verify the effectiveness of our recommendation system. Study results empirically demonstrated the importance of diversity of recommended products. The more diverse the product recommendations were, the higher the purchase rate, the average purchase amount, and the cross purchase rate were observed. In addition, we tracked the customers? purchase for two months after the user tests and found that diverse product exposure positively influenced customer retention (e.g., repurchase rate, amount).

Although virtual reality (VR) programs to provide firefighting training continue to be developed and adopted, our investigation with 15 firefighters indicates that a current VR training system tends to convey behavioral tips and does not sufficiently reflect actual firefighters’ needs and realities in the field. It often provides somewhat simplified fire simulations and actually lowers the effectiveness of the training. In this paper, we employ Human-Computer Interaction (HCI) methods to examine and identify core elements in firefighting scenarios and develop a VR system that incorporates such elements. We evaluate our system with respect to presence and three design components of the VR simulation (i.e., reality, meaning, play) through a user study with 22 participants. Our study results demonstrate greater user experience and perception toward the four elements in firefighting training with our VR system compared to the existing one. We discuss design implications (e.g., move control, degree of freedom, sight hindrance by smoke, unexpected events) of our study that are expected to help implement and provide an effective VR training system for firefighters.