In human-AI decision making, designing AI that complements human expertise has been a natural strategy to enhance human-AI collaboration, yet it often comes at the cost of decreased AI performance in areas of human strengths. This can inadvertently erode human trust and cause them to ignore AI advice precisely when it is most needed. Conversely, an aligned AI fosters trust yet risks reinforcing suboptimal human behavior and lowering human-AI team performance. In this paper, we start by identifying this fundamental tension between performance-boosting (i.e., complementarity) and trust-building (i.e., alignment) as an inherent limitation of the traditional approach for training a single AI model to assist human decision making. To overcome this, we introduce a novel human-centered adaptive AI ensemble that strategically toggles between two specialist AI models - the aligned model and the complementary model - based on contextual cues, using an elegantly simple yet provably near-optimal Rational Routing Shortcut mechanism. Comprehensive theoretical analyses elucidate why the adaptive AI ensemble is effective and when it yields maximum benefits. Moreover, experiments on both simulated and real-world data show that when humans are assisted by the adaptive AI ensemble in decision making, they can achieve significantly higher performance than when they are assisted by single AI models that are trained to either optimize for their independent performance or even the human-AI team performance.
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
Stanford University（斯坦福大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；The University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Southern California（南加州大学）；Salesforce Research（Salesforce研究院）
The System Hallucination Scale (SHS): A Human-Centered Instrument for Assessing Perceived Reliability of Large Language Models
Machine Learning and Information Science Group, Medical University of Graz（格拉茨医科大学机器学习与信息科学组）；Human Machine Mind Cooperation, Graz, Austria（格拉茨人类机智合作中心）；MIDATA Cooperative, Zurich, Switzerland（苏黎世MIDATA合作组织）；Human-Centered AI Lab, BOKU University Vienna（维也纳BOKU大学人本AI实验室）
Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making
Microsoft Research（微软研究院）；University of Virginia（弗吉尼亚大学）；University of Washington（华盛顿大学）；University of Michigan（密歇根大学）；Human Computation Institute（人类计算研究所）；LMU Munich（慕尼黑大学）；Western Washington University（西华盛顿大学）
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges
Hamad Bin Khalifa University（哈马德·本·哈利法大学）；The Hashemite University（哈希姆大学）；Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology（巴基斯坦-奥地利应用科学与技术学院）；Qatar University（卡塔尔大学）；Information Technology University（信息技术大学）
