Advancements in Data-Centric AI Foundations, Ethics, and Emerging Technology

Abstract

Data-centric artificial intelligence (data-centric AI) is an emerging paradigm that underscores the importance of systematically designing and engineering data to create AI-based systems that are both effective and efficient. The primary objective of this chapter is to familiarize practitioners and researchers in the field of information systems with the concept of data-centric AI. While data-centric AI is a captivating and nascent research area within the AI community, numerous organizations have already developed and maintained diverse data-centric applications aimed at generating high-quality data. These applications span a wide spectrum, encompassing traditional business data processing systems and advanced ML systems, such as a recommendation engine. Building data and ML platforms serving numerous applications across diverse organizations share valuable insights into lessons learned from data and ML engineering that can be applied in the context of data-centric AI. These insights can help enhance the design, development, and maintenance of data-centric AI systems. In conclusion, this theory serves as a comprehensive introduction to data-centric AI for IS practitioners and researchers, providing them with essential concepts, frameworks, and insights from the field of data and ML engineering. It sheds light on the significance of data-centric approaches and their implications for the future of AI and the IS community.

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