Data-Centric AI for reliable and responsible AI: from theory to practice
Mihaela van der Schaar · Isabelle Guyon · Nabeel Seedat · Jennifer Wortman Vaughan · Kyunghyun Cho · Razvan Pascanu · Jim Weatherall
La Nouvelle Orleans Ballroom A-C (level 2)
Data-Centric AI has recently been raised as an important paradigm shift in machine learning and AI — placing the previously undervalued “data work’ at the center of AI development. This tutorial aims to illuminate the fundamentals of Data-Centric AI and articulate its transformative potential. We will explore the motivation behind the data-centric approach, highlighting the power to improve model performance, engender more trustworthy, fair, and unbiased AI systems, as well as discuss benchmarking from a data-centric perspective. Our examination extends to standardized documentation frameworks, exposing how they form the backbone of this new paradigm. The tutorial will cover state-of-the-art methodologies that underscore these areas, which we will contextualize around the high-stakes setting of healthcare. A focus of this tutorial is providing participants with an interactive and hands-on experience. To this end, we provide coding/software tools and resources, thereby enabling practical engagement. The panel discussion, with experts spanning diverse industries, will provide a dynamic platform for discourse, enabling a nuanced understanding of the implications and limitations of Data-Centric AI across different contexts. Ultimately, our goal is that participants gain a practical foundation in data-centric AI, such that they can use or contribute to Data-Centric AI research.