Journal of Intelligence Information Systems
Special issue on “Data-Centric AI”

Editors
Donato Malerba, University of Bari Aldo Moro
Vincenzo Pasquadibisceglie, University of Bari Aldo Moro

The era of data-centric Artificial Intelligence marks a pivotal paradigm shift in both Artificial Intelligence (AI) and Machine Learning (ML), highlighting the construction of intelligent systems through the strategic utilization of high-quality data. This approach accentuates the significance of ensuring that information not only facilitates learning but also precisely targets the specific learning requirements of AI.
While AI has historically relied on data and algorithms, traditional model-centric AI treated data as static entities, focusing primarily on optimizing models for a given dataset. This approach has resulted in increasingly complex and opaque models, demanding larger volumes of training data. In contrast, the emerging data-centric AI is dedicated to systematically and algorithmically generating optimal data to feed ML models. The primary objective of data-centric AI approaches is to continually enhance data quality, enabling a level of model accuracy previously deemed unattainable through model-centric techniques alone.

This special issue aims to delve into the transformative impact of recent developments in the data-centric AI paradigm on the future of AI and ML. We invite contributions exploring how these advancements influence the development of intelligent systems across various domains such as business process development and maintenance, cybersecurity, Earth observation, bioinformatics, energy markets, smart cities, finance, and healthcare. We welcome both research-oriented and practical contributions that shed light on opportunities, perspectives, and open research directions within the data-centric AI paradigm, inspiring further innovations in the field.

Topics of interest for this special issue include, but not limited to:
High quality data preparation
• Data cleaning, denoising, and interpolation
• Novel feature engineering pipelines
• Label Errors and Confident Learning (CL)
• Selecting features and/or instances
• Performing outlier detection and removal
• Ensuring label consensus
• Producing consistent and low noise training data
• Extracting smart data form raw data
• Creating training datasets for small data problems
• Handling rare classes and explaining important class coverage in big data problems
• Incorporating human feedback into training datasets
• Combining multi-view, multi-source, multi-objective datasets
Data-centric ML and Deep Learning approaches
• Active learning to identify the most valuable examples to label
• Core-set learning to handle big data
• Semi-supervised learning, few-shot learning, weak supervision, confident learning to take advantage of limited amount of labels or handle label noise
• Transfer learning and self-supervised learning algorithms to achieve rich data representations to be used with scarceness of labels
• Concept drift detection to identify new data to label
• Adversarial learning to improve robustness and resilience
Responsible and Ethical AI
• Ensuring fairness, bias, ethics and diversity
• Green AI design and evaluation
• Scalable and reliable training
• Privacy-preserving and secure learning
• Reproducibility of AI
Data benchmark creation
• Creating licensed datasets based on public resources
• Creating high quality data from low quality resources
Data-centric Explainable AI
• Novel XAI methods to identify possible data issues in the learning stage
• XAI methods to generate features for machine learning problems
Applications of novel data-centric AI solutions


Expected Contributions and Submission Information
The submitted manuscripts (up to 25 pages including references, tables, and figures) must be written in English and describe original research neither published nor currently under review by other journals or conferences. Parallel submissions will not be accepted. All submissions will be peer-reviewed and judged on correctness, originality, technical strength, significance, quality of presentation, and relevance to the special issue topics of interest. The review process of JIIS is single-blinded. The submissions
should be prepared according to the guidelines of JIIS. These can be found
at https://www.springer.com/journal/10844/submission-guidelines.
All submissions should be done through the Springer Nature Snap system of
JIIS (https://submission.springernature.com/new-submission/10844/3) selecting “Data-Centric AI” in collections.
Important Deadlines
• Full paper submission deadline: February 28, 2024
• First-round review to authors: May 31, 2024
• Second-round submission deadline: July 31, 2024
• 2nd-round reviews to authors: September 30, 2024
• Online Publication: end of October - 2024 (tentative)

For questions or further information, please write to donato.malerba@uniba.it and vincenzo.pasquadibisceglie@uniba.it