Influence of anthropomorphic agent on human empathy through games
December 08, 2022 Β· Declared Dead Β· π IEEE Access
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Takahiro Tsumura, Seiji Yamada
arXiv ID
2212.04555
Category
cs.HC: Human-Computer Interaction
Citations
18
Venue
IEEE Access
Last Checked
4 months ago
Abstract
The social acceptance of AI agents, including intelligent virtual agents and physical robots, is becoming more important for the integration of AI into human society. Although the agents used in human society share various tasks with humans, their cooperation may frequently reduce the task performance. One way to improve the relationship between humans and AI agents is to have humans empathize with the agents. By empathizing, humans feel positively and kindly toward agents, which makes it easier to accept them. In this study, we focus on tasks in which humans and agents have various interactions together, and we investigate the properties of agents that significantly influence human empathy toward the agents. To investigate the effects of task content, difficulty, task completion, and an agent's expression on human empathy, two experiments were conducted. The results of the two experiments showed that human empathy toward the agent was difficult to maintain with only task factors, and that the agent's expression was able to maintain human empathy. In addition, a higher task difficulty reduced the decrease in human empathy, regardless of task content. These results demonstrate that an AI agent's properties play an important role in helping humans accept them.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted