Special Issue on Knowledge Enhanced Data Analytics for Autonomous Decision Making
摘要截稿:
全文截稿: 2020-04-30
影响因子: 2.678
期刊难度:
CCF分类: B类
中科院JCR分区:
• 大类 : 计算机科学 - 3区
• 小类 : 计算机:人工智能 - 3区
Overview
In today’s world, we are aware that how breakthroughs in data analytics and high-performance computing has made society-changing AI applications in different areas. One particular stand out success relates to learning from a massive amount of data in real time to quickly identify newly emerging unknown patterns. However, successful decision-making analysis must combine the best qualities of both human analysts and computers, while the challenge is how to structure relevant and reliable knowledge and incorporate them as part of decision analytics. On the one hand, decision making needs the context, organization, and consistency that analytics by itself does not provide. There is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. On the other hand, autonomous decision-making and the black-box design of machine learning make the adoption of AI systems complicated and has led to resurgence in interest in explainability of AI systems.
This Special Issue aims to demonstrate the indispensable role of business, data and methodological know-how in helping decision-making and how to use and exploit the prior knowledge to enhance data analytic for autonomous decision-making.
Topics
We are seeking both conceptual and empirical papers offering new insights and contribution to the development of data analytic algorithms and systems for autonomous decision-making, which focus on the following topics (but are not limited to) which demonstrate the role of exploiting the knowledge to enhance data analytics:
• Application that have limited data;
• Applications require safety or stability guarantees;
• Applications while large amounts of quality training data are unavailable;
• Application while the objects to be recognized are complex, (e.g., implicit entities and highly subjective content);
• Applications need to use complementary or related data in multiple modalities/media;
• Enhancing the capability in handling uncertainty;
• Enhancing transparency, interpretability and explainability;
• Reducing the complexity of model architecture in time and space;
• Enhancing the capability to avoid social discrimination and unfair treatment;
• Enhancing automated decision making capability and performance;
• Enhancing reliability and integrity of data analytics.