Trust calibration is essential in AI-assisted decision-making tasks. If human users understand the reasons for a prediction of an AI model, they can assess whether or not the prediction is reasonable. Especially for high-risk tasks like mushroom hunting (where a … Continued
Much literature has shown that prompt-based learning is an efficient method to make use of the large pre-trained language model. Recent works also exhibit the possibility of steering a chatbot’s output by plugging in an appropriate prompt. Gradient-based methods are … Continued
This work proposes a framework that incorporates trust in an ad hoc teamwork scenario with human-agent teams, where an agent must collaborate with a human to perform a task. During the task, the agent must infer, through interactions and observations, … Continued
Abstract The use of a moderator has become ubiquitous when using focus groups for social science research. While a skilled moderator can facilitate discussion, we argue that moderators can potentially also hinder the generation of the types of group discussions … Continued
Trust is fundamental to effective visual data communication between the visualization designer and the reader. Although personal experience and preference influence readers’ trust in visualizations, visualization designers can leverage design techniques to create visualizations that evoke a “calibrated trust,” at … Continued
https://psyarxiv.com/ad7gw/ Decision-makers in diverse fields ranging from medicine to law to public policy depend on the clear and unbiased communication of probabilities. Expert judges often express probabilities verbally (e.g., unlikely) rather than numerically (e.g., 25% chance). Researchers have criticized the … Continued
https://arxiv.org/abs/2209.13578 Artificial intelligence (AI) systems are increasingly used for providing advice to facilitate human decision making. While a large body of work has explored how AI systems can be optimized to produce accurate and fair advice and how algorithmic advice … Continued
Explanations have been framed as an essential feature for better and fairer human-AI decision-making. In the context of fairness, this has not been appropriately studied, as prior works have mostly evaluated explanations based on their effects on people’s perceptions. We … Continued
https://psyarxiv.com/yahvf/ Abstract Many online content providers use “personalization” algorithms to generate recommendations that are individually fine-tuned for users’ interests. However, these algorithms have also been criticized because tailoring content to specific users necessarily restricts the diversity of content, leading to … Continued
Artificial Intelligence (AI) covers a broad spectrum of computational problems and use cases. Many of those implicate profound and sometimes intricate questions of how humans interact or should interact with AIs. Moreover, many users or future users do have abstract … Continued