SusBench: An Online Benchmark for Evaluating Dark Pattern Susceptibility of Computer-Use Agents
Proceedings of the 31st International Conference on Intelligent User Interfaces (IUI 2026). Acceptance rate: 20%.
Robert Wolfe
Robert Wolfe is an Assistant Professor in the Rutgers iSchool, where his research focuses on understanding and improving the reliability of general-purpose AI models. Wolfe received his Ph.D. in Information Science from the University of Washington for his work on epistemic risks in generative and general-purpose AI. He previously earned Master’s degrees in Computer Science, Information Science, and English Literature. Wolfe’s ongoing work studies evolving privacy norms around chatbots, applications of AI in high-stakes information work, and uses of AI on social media. His recent collaborations on topics including privacy attacks on large language models, calibration methods for general-purpose AI, justice-oriented neighborhood technologies, trust-building approaches on social media, and novel approaches to AI literacy have been published in venues including ACM CHI, CSCW, SOUPS, ICML, ACL, and IDC.
Publications
Understanding Privacy Norms Around LLM-Based Chatbots: A Contextual Integrity Perspective
Proceedings of the 2025 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2025). Acceptance rate: 32%.
Trust-Enabled Privacy: Social Media Designs to Support Adolescent User Boundary Regulation
Proceedings of the 21st Symposium on Usable Privacy and Security (SOUPS 2025).
Privacy as Social Norm: Systematically Empowering Teen Privacy Management on Social Media
Proceedings of the ACM on Human-Computer Interaction (CSCW), 9(2):CSCW151, 2025.
Toward Nonviolent Design: Co-Designing a Human-Centered Framework for AI-Mediated Communication
Proceedings of the 2025 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2025). Acceptance rate: 32%.
Building the Beloved Community: Designing Technologies for Neighborhood Safety
Proceedings of the 2025 ACM Conference on Human Factors in Computing Systems (CHI 2025). Acceptance rate: 25%.
Best paper honorable mention (top 5%)
Representation Bias of Adolescents in AI: A Bilingual, Bicultural Study
Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2024). Acceptance rate: 38%.
ML-EAT: A Multilevel Embedding Association Test for Interpretable and Transparent Social Science
Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2024). Acceptance rate: 38%.
Dataset Scale and Societal Consistency Mediate Facial Impression Bias in Vision-Language AI
Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society (AIES 2024). Acceptance rate: 38%.
Sharing, Not Showing Off: How BeReal Approaches Authentic Self-Presentation on Social Media Through Its Design
Proceedings of the ACM on Human-Computer Interaction (CSCW), 8(CSCW2):1-32, 2024.