Human-AI Interaction

insight - Human-AI Interaction

Framing Uncertainty in AI Decision Aids: Longitudinal Impact on User Trust - A Gig Driver Case Study

The framing of uncertainty in the outcomes of AI-based decision aids can significantly impact users' longitudinal trust and willingness to rely on the system.

Interactively Guiding AI Attention Does Not Always Improve Human-AI Team Accuracy in Fine-Grained Image Classification

Allowing humans to interactively guide machine attention does not consistently improve the accuracy of human-AI teams in fine-grained image classification tasks.

Fair Machine Guidance to Educate Individuals on Unbiased Decision-Making

Fair machine guidance aims to educate individuals on making unbiased decisions by leveraging fairness-aware machine learning to identify and address their biases.

Exploring Human-Large Language Model Interaction Patterns: Insights into Collaboration and Creativity

This study presents a comprehensive mapping of the current research landscape on human interaction patterns with large language models, focusing on the perspectives of collaboration and creativity.

Detecting and Mitigating Hallucinations in Large Language Model-Generated Content: Insights from Human Perception and Engagement

Humans can discern the relative accuracy of LLM-generated content, ranking it as genuine > minor hallucination > major hallucination. Warning labels reduce the perceived accuracy and increase dislike of hallucinated content, without significantly affecting genuine content.

Evolving Agents: Simulating Dynamic and Diverse Human Personalities through Interactive Experiences

Evolving Agents is a novel agent architecture that enables agents to exhibit differentiated behaviors corresponding to their diverse personalities and undergo continuous personality evolution based on external information during interactive simulation.

Collaborative Human-AI Trust (CHAI-T): A Process Framework for Active Management of Trust in Human-AI Collaboration

The core message of this article is to propose a process framework for understanding and managing trust in collaborative human-AI (HAI) teams. The framework, called CHAI-T, incorporates the context-specificity, team processes, and temporal dynamics that characterize trust development and maintenance in HAI teaming.

Empowering Users to Efficiently Leverage Large Language Models Through Low-code Interaction

A novel human-LLM interaction framework, Low-code LLM, incorporates simple low-code visual programming interactions to achieve more controllable and stable responses from LLMs for complex tasks.

Cognitive Biases in Human-AI Collaboration: Anthropomorphism and Framing Effects in Hiring Decisions

Cognitive biases, such as anthropomorphism and framing effect, can significantly impact human agreement with AI recommendations in hiring decisions, highlighting the need for tailored approaches to AI product design.

A Taxonomy of Interaction Modes for Enhancing Human-Large Language Model Collaboration

This paper presents a taxonomy that categorizes the various interaction modes between humans and large language models (LLMs), aiming to empower users to tackle complex tasks by utilizing LLMs beyond the default conversational prompting paradigm.

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