Trust biases how users rely on AI recommendations in AI-assisted decision-making tasks, with low and high levels of trust resulting in increased under- and over-reliance, respectively. We propose that AI assistants should adapt their behavior through trust-adaptive interventions to mitigate … Continued
AI systems increasingly support human decision-making. In many cases, despite the algorithm’s superior performance, the final decision remains in human hands. For example, an AI may assist doctors in determining which diagnostic tests to run, but the doctor ultimately makes … Continued
Despite widespread speculation about artificial intelligence’s impact on the future of work, we lack systematic empirical evidence about how these systems are actually being used for different tasks. Here, we present a novel framework for measuring AI usage patterns across … Continued
As artificial intelligence (AI) systems, particularly large language models (LLMs), become increasingly integrated into decision-making processes, the ability to trust their outputs is crucial. To earn human trust, LLMs must be well calibrated such that they can accurately assess and … Continued
As AIs rapidly advance and become more agentic, the risk they pose is governed not only by their capabilities but increasingly by their propensities, including goals and values. Tracking the emergence of goals and values has proven a longstanding problem, … Continued
Most public audiences in Germany receive scientific information via a variety of (digital) media; in these contexts, media act as intermediaries of trust in science by providing information that present reasons for public audiences to place their trust in science. … Continued
Growing partisan gaps in confidence in scientists and scientific institutions, and other divisions among Americans across social identities, call for novel approaches to science communication. But common assumptions can often mask the nuanced nature of these divisions and the potential … Continued
We argue that advancing LLM-based human simulation requires addressing both LLM’s inherent limitations and simulation framework design challenges. Recent studies have revealed significant gaps between LLM-based human simulations and real-world observations, highlighting these dual challenges. To address these gaps, we … Continued
The rapid evolution of large language models (LLMs) has transformed human-computer interaction (HCI), but the interaction with LLMs is currently mainly focused on text-based interactions, while other multi-model approaches remain under-explored. This paper introduces VTutor, an open-source Software Development Kit … Continued
Abstract The rapid proliferation of Generative Artificial Intelligence (Gen AI) has created an intriguing paradox in organizations: while employees increasingly recognize Gen AI’s potential to enhance productivity and innovation, many use these tools covertly. Drawing on dual-process theory, we introduce … Continued