ACM Transactions on Interactive Intelligent Systems
Scope & Guideline
Fostering Interdisciplinary Innovations in Intelligent Systems.
Introduction
Aims and Scopes
- Human-AI Collaboration:
The journal explores how humans and AI can work together effectively, focusing on trust, transparency, and the dynamics of human-AI teams. - Explainable AI (XAI):
A significant emphasis is placed on developing AI systems that can explain their reasoning and decisions to users, enhancing trust and usability. - Interactive Data Visualization:
Research in this area focuses on improving how users interact with complex data through visual means, including explorations into user-centered design and analytics. - User Experience and Interaction Design:
The journal covers methodologies for designing interfaces that enhance user experience, particularly in the context of intelligent systems. - Ethics and Bias in AI:
TiiS addresses the ethical implications of AI technologies, including fairness, bias mitigation, and responsible AI use. - Adaptive and Personalized Systems:
Research often investigates how systems can adapt to individual user needs and preferences, improving the overall interaction experience. - Social and Emotional AI:
The journal explores the development of AI that can recognize and respond to human emotions and social cues, facilitating better interactions.
Trending and Emerging
- Trust and Transparency in AI Systems:
Recent publications emphasize understanding how users can trust AI systems, focusing on transparency and explainability as critical factors in user acceptance. - AI Ethics and Fairness:
There is a growing trend towards addressing ethical concerns related to AI, particularly focusing on bias mitigation and fairness in AI applications. - Natural Language Interfaces:
The popularity of natural language processing and conversational interfaces is increasing, with a focus on how they can facilitate better user interactions and data exploration. - Interactive Machine Learning:
Research on integrating user feedback into machine learning processes is trending, highlighting the importance of human input in refining AI models. - Visual Analytics and Data Interpretation:
Emerging themes include the development of tools and techniques for visualizing complex data, enhancing user understanding and decision-making through interactive analytics. - Personalization Techniques in Intelligent Systems:
An increased focus on adaptive systems that cater to individual user preferences and contexts is evident, reflecting the demand for more personalized interactions.
Declining or Waning
- Traditional User Interface Design:
As intelligent systems become more integrated into everyday applications, traditional user interface design approaches that do not incorporate AI or adaptive elements are becoming less prominent. - General AI Applications without Human-Centric Focus:
Papers discussing AI applications without emphasizing human interaction or user experience are appearing less frequently, suggesting a shift towards more user-centered AI research. - Basic Interaction Techniques:
Research focusing solely on basic interaction techniques without the incorporation of intelligent systems or data-driven insights is declining, as the field moves towards more complex and integrated solutions. - Non-interactive AI Systems:
There is a noticeable reduction in the exploration of AI systems that do not involve interactive components or user engagement, aligning with the journal’s current focus on interactivity.
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