Over the past decade, Artificial Intelligence (AI) solutions have been driven by the model-centric paradigm, which neglects to some extent the power of data and its role in the data science lifecycle. Models learn from data, and when applied to real-world domains, are inevitably faced with data properties that naturally compromise their behaviour (e.g., missing data, class overlap, imbalanced data, biased data, noisy data, leaky data).

Therefore, an important part of the success of AI solutions is their ability to transform imperfect data into intelligent data, i.e., data of sufficient quality to allow classifiers to draw accurate and reliable inferences on the domains. This process of moving from imperfect to intelligent data in a systematic and continuous manner has given rise to the data-centric AI paradigm, where data becomes the center piece of successful AI applications both in research and the industry alike.

The Thematic Track “Data-Centric AI – Solutions and Emerging Technologies” addresses practical and theoretical contributions related to the use of Data-Centric AI solutions, focusing on cutting-edge research, methodologies, tools, and applications. We encourage researchers and practitioners to conceptualise solutions that revolve around improving data, not models, to ensure efficient and responsible downstream applications.