Yanyun Wang

Mengke Wu, Weizi Liu, Yanyun Wang et al.

AI-driven recommender systems are often perceived as personalization black boxes, limiting users' ability to understand how their data shapes content (information asymmetry) or to influence system behavior meaningfully (power asymmetry). This study explores how design can strengthen user agency by integrating transparency with actionable control. We developed a provotype that introduces new interface features for managing data use, discovering varied content, and configuring context-based recommending modes. The walkthroughs and interviews with 19 participants show how these features help users interpret personalization signals, understand how their actions influence outcomes, address concerns from unwanted inference to narrow feeds (e.g., filter bubbles), and build trust in the system. We also identify strategies for promoting adoption and awareness of agency-enhancing features. Overall, our findings reaffirm users' desire for active influence over personalization and contribute concrete interface mechanisms with empirical insights for designing recommender systems that foreground user autonomy and fairness in AI-driven content delivery.

Yanyun Wang, Li Liu

Adversarial Training (AT) is one of the most effective methods to train robust Deep Neural Networks (DNNs). However, AT creates an inherent trade-off between clean accuracy and adversarial robustness, which is commonly attributed to the more complicated decision boundary caused by the insufficient learning of hard adversarial samples. In this work, we reveal a counterintuitive fact for the first time: From the perspective of perception consistency, hard adversarial samples that can still attack the robust model after AT are already learned better than those successfully defended. Thus, different from previous views, we argue that it is rather the over-sufficient learning of hard adversarial samples that degrades the decision boundary and contributes to the trade-off problem. Specifically, the excessive pursuit of perception consistency would force the model to view the perturbations as noise and ignore the information within them, which should have been utilized to induce a smoother perception transition towards the decision boundary to support its establishment to an appropriate location. In response, we define a new AT objective named Robust Perception, encouraging the model perception to change smoothly with input perturbations, based on which we propose a novel Robust Perception Adversarial Training (RPAT) method, effectively mitigating the current accuracy-robustness trade-off. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet with ResNet-18, PreActResNet-18, and WideResNet-34-10 demonstrate the effectiveness of our method beyond four common baselines and 12 state-of-the-art (SOTA) works. The code is available at https://github.com/FlaAI/RPAT.