The Foundation of Trustworthy AI: Data Engineering’s Critical Role

Shishir Tewari ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2025, vol. 8, issue 1, 219-231

Abstract: As artificial intelligence (AI) integrates deeper into enterprise workflows, establishing trust in AI systems has become a paramount concern. This article highlights the pivotal role of data engineering in fostering responsible AI development through rigorous data quality, governance, and fairness practices. Using real-world examples from finance, healthcare, and retail, we showcase how critical data engineering techniques—such as data validation, master data management, and bias mitigation—enhance the reliability, compliance, and ethical soundness of AI solutions. We further explore emerging innovations like data mesh, Lakehouse architectures, and real-time data pipelines, emphasizing their potential to scale trustworthy AI in cloud-first environments. By treating data as a strategic asset and applying disciplined engineering principles, organizations can develop AI systems that are not only high-performing but also transparent, equitable, and aligned with regulatory and societal standards.

Keywords: Data Engineering; Responsible AI; Data Governance; Master Data Management; AI Trust; Data Quality; Ethical AI; Bias Mitigation (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://newjaigs.com/index.php/JAIGS/article/view/369 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:das:njaigs:v:8:y:2025:i:1:p:219-231:id:369

Access Statistics for this article

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 is currently edited by Justyna Żywiołek

More articles in Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 from Open Knowledge
Bibliographic data for series maintained by Open Knowledge ().