CFP: DEARING 2024 - 1st International Workshop on Data-Centric Artificial Intelligence” workshop event co-located with the ECML PKDD 2024 conference, and will take place on September 9-13, 2024, in Vilnius, Lithuania

The event will provide an opportunity to delve into the latest developments, fostering discussions and collaborations that contribute to the ongoing evolution of data-centric AI. We invite participants who share a commitment to advancing the field through the exploration and application of cutting-edge techniques, ultimately shaping the future landscape of artificial intelligence.

We welcome submissions that explore the opportunities, perspectives, and research directions within the Data-Centric AI paradigm. Potential topics include, but are not limited to:

High-quality data preparation:

-Data cleaning, denoising, and interpolation
-Novel feature engineering pipelines
-Performing outlier detection and removal
-Ensuring label consensus
-Producing consistent and low-noise training data
-Extracting smart data from 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 the 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 and management
-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

DEARING welcomes both research papers reporting results from mature work, as well as more speculative papers describing new ideas or preliminary exploratory work. Papers reporting industry experiences and case studies will also be encouraged. Submissions are accepted in two formats:

Regular research papers with 12 to 16 pages including references.
Short research papers of at most 6 pages including references. Research statements aim at fostering discussion and collaboration. (eg. outline new researching ideas)
All submissions should be made in PDF using the Microsoft CMT platform and must adhere to the Springer LNCS style.

All workshop papers will be included in a joint Post-Workshop proceeding published by Springer Communications in Computer and Information Science in 1-2 volumes, organized by focused scope and possibly indexed by WOS. Paper authors will have the faculty to opt-in or opt-out. We suggest workshop papers be prepared and submitted in the LNCS format.

To be published in the proceedings, research papers must be original, not published previously, and not submitted concurrently elsewhere.

Organizing Committee:
Angela Bonifati - Claude Bernard University Lyon 1
Veronica Guidetti - University of Modena and Reggio Emilia
Donato Malerba - University of Bari Aldo Moro
Federica Mandreoli - University of Modena and Reggio Emilia
Vincenzo Pasquadibisceglie - University of Bari Aldo Moro

Paper Submission deadline: 2024-06-15
Paper Author notification: 2024-07-15

More info: https://dearing2024.di.uniba.it/landing