Human-Computer Interaction (HCI) in the AI context examines the design, evaluation, and implementation of interactive systems that incorporate artificial intelligence capabilities. This interdisciplinary field addresses usability, accessibility, user experience, and cognitive aspects of AI-powered interfaces, emphasising transparency, trust calibration, and ethical implications of algorithmic decision-making on human users.
Semantic Classification
Content
Key Characteristics
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Designs intuitive interfaces for AI-powered systems
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Incorporates explainability and transparency mechanisms
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Evaluates user trust and acceptance of AI recommendations
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Addresses accessibility and inclusive design principles
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Facilitates human-AI collaboration and co-creation
Overview
Human-Computer Interaction (HCI) in the AI context examines the design, evaluation, and implementation of interactive systems that incorporate artificial intelligence capabilities. This interdisciplinary field addresses usability, accessibility, user experience, and cognitive aspects of AI-powered interfaces. Key areas include conversational AI (chatbots, voice assistants), explainable AI interfaces, mixed-initiative systems, and adaptive user interfaces that personalize based on user behavior. HCI for AI emphasizes transparency, user control, trust calibration, and the ethical implications of algorithmic decision-making on human users.
Related Concepts
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References
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Amershi, S. et al. (2019). Guidelines for Human-AI Interaction. CHI 2019.
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Shneiderman, B. (2020). Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy. International Journal of Human-Computer Interaction, 36(6), 495-504.
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Yang, Q. et al. (2020). Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design. CHI 2020.