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IIKE 2019 - 1st International Workshop on Intelligence & Interaction in Knowledge Engineering

Date2019-01-30 - 2019-02-01


VenueNewport Beach, CA, USA - United States USA - United States



Topics/Call fo Papers

Recent efforts in AI driven knowledge engineering and machine learning have largely focused on improving the accuracy and efficiency, and many automated techniques are perceived as a black box to the human with little justification or means to intervene in various modelling and training processes. This lack of ability to explain to the human why a certain output was generated has potentially limited the effectiveness and reliability of knowledge-based systems. This is echoed by DARPA’s recent initiative on Explainable Artificial Intelligence (XAI) as well as the growing area of Interactive Machine Learning (IML), which call for a new generation of intelligent systems that are human-centered with explainable models and interactive interfaces enabling the human to understand, engage with, and influence the underlying algorithms. As such, appropriate integrations of user interaction techniques lie at the center of knowledge-based systems, where interactive data representation, data flows, and analytical techniques may provide a necessary meaningful platform for humans to better understand, explore, correct and modify intelligent models. The 1st International Workshop on Intelligence & Interaction in Knowledge Engineering (IIKE) aims to bring researchers interested in interactive and explainable intelligent models, theories, and techniques. IIKE’s goal is to facilitate and accelerate research in all aspects of knowledge engineering for intelligent systems by exchanging ideas, approaches, results, and cross-disciplinary collaboration.
Themes of interest
Themes of interest include, but are not limited to, the following:
Interaction in knowledge discovery, representation, acquisition, and integration
Human-centered knowledge systems
Evaluation strategies, models, and approaches
Explanation of learning reasoning strategies
Interactive debugging techniques
Transparency and feedback in AI
Trust assessment in intelligent systems
Evaluation strategies, models, and approaches
Approaches and Application domains (including but not limited to): cognitive modelling , deep learning, NLP, Computational neuroscience, Knowledge-driven AI Governance, ...

Last modified: 2018-10-09 20:58:42