Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds of individuals correlate in practice with different modes of human-computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia.
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
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）
Having our omic cake and eating it too: Evaluating User Response to using Blockchain Technology for Private & Secure Health Data Management and Sharing
Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment
Imperial College London（伦敦帝国学院）；Technical University of Munich（慕尼黑工业大学）；Konrad Zuse School of Excellence in Reliable AI（康拉德·楚泽可靠人工智能卓越学院）；Munich Data Science Institute（慕尼黑数据科学研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s)
School of Social Work, University of Michigan（密歇根大学社会工作学院）；Graduate School of Social Service, Fordham University（福特汉姆大学社会工作研究生院）；School of Social Work, Wayne State University（韦恩州立大学社会工作学院）
Evaluating Reasoning LLMs for Suicide Screening with the Columbia-Suicide Severity Rating Scale
Ira A. Fulton Schools of Engineering Arizona State University（亚利桑那州立大学工程学院）；Heinz College of Information Systems and Public Policy Carnegie Mellon University（卡内基梅隆大学信息系统与公共政策学院）；School of Education and Counseling Psychology Santa Clara University（圣克拉拉大学教育与咨询心理学学院）
