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    Area of Science:

    • Artificial Intelligence
    • Human-Computer Interaction
    • Cognitive Science

    Background:

    • Effective communication is vital for human-AI collaboration in problem-solving and decision-making.
    • Understanding the dynamics of explanations and interactions between human and AI agents is crucial to prevent miscommunication.

    Purpose of the Study:

    • To propose a communication dynamics model for human-AI interaction.
    • To examine how a sender's explanation intention and strategy influence a receiver's perception of explanation effects.
    • To identify potential biases and reasoning pitfalls in human-AI communication.

    Main Methods:

    • Development of a theoretical communication dynamics model.
    • Analysis of sender explanation strategies and receiver perception.
    • Identification of cognitive biases and reasoning pitfalls in human-AI interactions.

    Main Results:

    • The study models the impact of sender's explanation intention and strategy on receiver's perception.
    • Potential biases and reasoning pitfalls in human-AI communication are identified.
    • Six desiderata for human-centered explainable AI are proposed.

    Conclusions:

    • The proposed model contributes to understanding and designing more effective hybrid intelligence systems.
    • Addressing communication dynamics is key to mitigating miscommunication in human-AI collaboration.
    • The research outlines future directions for human-centered explainable AI.