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Agents, Robotics, and Social Interaction / Trust, Transparency, and Response Latency Design

Users lack trust because the chatbot's information generation process is opaque.

lightbulbPractical problemTrust, Transparency, and Response Latency Design

Users lack trust because the chatbot's information generation process is opaque.

CUI '24Explaining Delays: How to Provide Reasonable Explanations for Chatbot Response Times to Influence User Trust

Similar questions

lightbulbPractical problemTrust, Transparency, and Response Latency Design

Users easily leak sensitive personal information when interacting with chatbots but lack convenient, effective privacy protection tools.

CHI '25Rescriber: Smaller-LLM-Powered User-Led Data Minimization for LLM-Based Chatbots

lightbulbPractical problemTrust, Transparency, and Response Latency Design

Public spaces lack natural, safe, and engaging human-robot interaction.

CHI '25Encounter with the Giants: Understanding Interaction with Large-scale Inflatable Soft Robots

lightbulbPractical problemTrust, Transparency, and Response Latency Design

Chatbots appear inhuman and unnatural because they generate responses too quickly.

CUI '24Beyond Words: Infusing Conversational Agents with Human-like Typing Behaviors

lightbulbPractical problemTrust, Transparency, and Response Latency Design

Users often feel that empathy expressed by robots lacks depth and may even cause harm.

CHI '24The Illusion of Empathy? Notes on Displays of Emotion in Human-Computer Interaction

lightbulbPractical problemTrust, Transparency, and Response Latency Design

Users may distrust chatbot recommendations and experience low efficiency in multitask scenarios.

CUI '23Chatbots as Advisers: the Effects of Response Variability and Reply Suggestion Buttons

lightbulbPractical problemTrust, Transparency, and Response Latency Design

Chatbot replies often appear correct yet contextually inappropriate, making them difficult to debug.

CUI '23Democratizing Chatbot Debugging: A Computational Framework for Evaluating and Explaining Inappropriate Chatbot Responses

Related papers

Rescriber: Smaller-LLM-Powered User-Led Data Minimization for LLM-Based Chatbots

Jijie Zhou, Eryue Xu, Yaoyao Wu

Encounter with the Giants: Understanding Interaction with Large-scale Inflatable Soft Robots

Bijetri Biswas Biswas, Emma Powell, Robert Nixdorf

Beyond Words: Infusing Conversational Agents with Human-like Typing Behaviors

Jijie Zhou, Yuhan Hu

The Illusion of Empathy? Notes on Displays of Emotion in Human-Computer Interaction

Andrea Cuadra, Maria Wang, Lynn Andrea Stein

Chatbots as Advisers: the Effects of Response Variability and Reply Suggestion Buttons

Federico Milana, Enrico Costanza, Joel E Fischer

The Bot on Speaking Terms: The Effects of Conversation Architecture on Perceptions of Conversational Agents

Christina Ziying Wei, Young-Ho Kim, Anastasia Kuzminykh

Disagree? You Must Be a Bot! How Beliefs Shape Twitter Profile Perceptions

Magdalena Wischnewski, Rebecca Bernemann, Thao Ngo