Call for Papers: Special Issue on Data Science for Responsible Data Management
In the digital era with advancements in innovative technologies, data are being generated and collected at an unprecedented pace. A data-driven world opens up tremendous possibilities and opportunities for individuals, businesses and governments to make use of their data assets to create value. Data-driven decision-making using AI-powered technologies is sweeping through all aspects of society, ranging from business applications such as autonomous vehicles to their use in the criminal justice system. Although AI technologies have been tremendously successful, they critically depend on the quality of the underlying data and data science pipelines. For example, poor quality data prevalent in real-world data sets can have serious adverse consequences on the quality of decisions made using AI. Similarly, without effectively modelling large-scale, unstructured, multi-modal and complex data, achieving human understandable machine intelligence for decision making would be challenging. Responsible data management promotes best practices that maximize the availability and ethical use of high-quality data while ensuring accurate, robust and interpretable AI-powered decision making. It requires innovations in the data science pipeline from data generation to analysis that can explain insights and outcomes produced by AI technologies. Research efforts from multiple disciplines that contribute to data science including data mining, machine learning, statistics, social science, etc. are needed to enable responsible data management.
In this special issue, we solicit innovative research articles on data science that can enable responsible data management.
Topics of interests include, but not limited to, the following:
- Data generation and collection
- Data cleansing and selection for effective learning
- Data benchmarking for real-world AI
- Data privacy for distributed learning
- Multimodal data integration and learning paradigms
- Real-time data analytics and AI practices
- Scalable AI with knowledge graph
- Data/knowledge-driven explainable AI
- Findable, accessible, interoperable and reusable (FAIR) data
This Special Issue encourages members of our community to conduct cross-disciplinary work on the topic of data science for responsible data management.
* Helen Zi Huang, University of Queensland, Australia
* Yanyan Shen, Shanghai Jiaotong University, China.
* Divesh Srivastava, AT&T, USA
Initial Submission Date: extended to February 21st, 2021
First Round Decision: May 4th, 2021
Manuscript Revision: July 14th, 2021
Final Decision Notification: September 13, 2021
* Authors are encouraged to submit high-quality, original work that has neither appeared in nor is under consideration by other journals.
* All papers will be reviewed following standard reviewing procedures for the VLDB Journal.
* Papers must be prepared in accordance with the VLDB Journal guidelines: https://www.springer.com/journal/778.
* Submit manuscripts to: http://VLDB.edmgr.com (under “Article Type” choose “S.I.: Responsible”)
* Springer provides a host of information about publishing in a Springer Journal on the information page for journal article authors, including FAQs, Tutorials along with Help and Support.