Key Takeaways on Data Engineering for Responsible AI

• Data infrastructure is the overlooked foundation of AI systems: While AI models receive significant attention, organizations struggle most with the data infrastructure needed to support them. The paper found that data engineering has received far less research focus than modeling, despite being essential for AI systems to function at all.

• Data engineering requires specialized expertise: There is a growing need for data engineers who understand how to prepare data specifically for AI systems and can build enterprise-wide data architectures. This suggests that general software engineering skills are insufficient for responsible AI deployment.

• The field lacks documented best practices: With only 25 relevant papers identified between 2019-2023, the mapping study reveals significant gaps in documented lessons learned and best practices for AI data engineering. This knowledge gap makes it harder for organizations to implement responsible data practices consistently.

• Multiple lifecycle phases need attention: The study identifies that different phases of AI data engineering require coverage and solutions, implying that responsible AI requires attention throughout the entire data lifecycle—not just at the point of model deployment.

• Practitioners need practical guidance: The paper emphasizes implications for both practitioners seeking solutions and researchers identifying gaps, suggesting that bridging the gap between research and practice is critical for improving how organizations handle data in AI systems.