Accepted to NeurIPS 2020 Workshop on Dataset Curation and Security; Also accepted at Navigating the Broader Impacts of AI Research Workshop. All authors contributed equally. The list of authors is arranged alphabetically
This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects. Many papers that deploy such attacks characterize themselves as "real world." Despite this framing, however, we found the physical or real-world testing conducted was minimal, provided few details about testing subjects and was often conducted as an afterthought or demonstration. Adversarial ML research without representative trials or testing is an ethical, scientific, and health/safety issue that can cause real harms. We introduce the problem and our methodology, and then critique the physical domain testing methodologies employed by papers in the field. We then explore various barriers to more inclusive physical testing in adversarial ML and offer recommendations to improve such testing notwithstanding these challenges.
Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions
Urban Information Lab（城市信息实验室）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Department of Computer Science（计算机科学系）；McCombs School of Business（麦库姆斯商学院）；IBM Research（IBM 研究院）
Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications
ByWater Institute, Tulane University（杜兰大学拜沃特研究所）；Office of Teaching, Learning, and Technology, University of Iowa（爱荷华大学教学、学习与技术办公室）；River-Coastal Science and Engineering, Tulane University（杜兰大学河流与海岸科学与工程系）
本研究评估了在R1公立大学工程本科课程中应用的AI学习框架Educational AI Hub，通过混合方法发现学生认可其便利性但存在伦理顾虑，强调需提升可用性、伦理透明度并辅以教师指导以促进有效AI学习参与。
Antisocial Analagous Behavior, Alignment and Human Impact of Google AI Systems: Evaluating through the lens of modified Antisocial Behavior Criteria by Human Interaction, Independent LLM Analysis, and AI Self-Reflection
