Humanizing Data Science Through Domain Expertise for Ethical and Trustworthy Artificial Intelligence

Marviola Hardini; Nur Azizah; Syahla Naurah; Rendhika Adyatama; Souza Nurafrianto Windiartono Putra

doi:10.68012/jot.v3i1.111

Authors

Marviola Hardini Indonesian Lecturers Association https://orcid.org/0000-0003-3336-2131
Nur Azizah University of Raharja https://orcid.org/0000-0002-5333-5520
Syahla Naurah University of Raharja
Rendhika Adyatama University of Raharja https://orcid.org/0009-0008-7071-5374
Souza Nurafrianto Windiartono Putra Universitat des Saarlandes

DOI:

https://doi.org/10.68012/jot.v3i1.111

Keywords:

Humanizing Data Science, Domain Expertise, Ethical Artificial Intelligence, Trustworthy AI, Systematic Literature Review

Abstract

The rapid adoption of Artificial Intelligence (AI) has transformed data-driven decision making across healthcare, finance, education, and public services. De spite these advances, AI systems continue to face challenges related to algorithmic bias, limited contextual understanding, insufficient transparency, and declining user trust, highlighting the need to integrate human expertise throughout the data science process. This study aims to systematically examine the role of domain expertise in Humanizing Data Science for the development of Ethical and Trustworthy Artificial Intelligence. A Systematic Literature Review (SLR) was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Peer-reviewed studies published be tween 2021 & 2026 were identified, screened using predefined inclusion and exclusion criteria, and analyzed through thematic synthesis. The review identifies five major themes, namely Human-Centered Artificial Intelligence, Domain Expertise Integration, Ethical and Trustworthy AI, Explainability and Human Oversight, and Humanizing Data Science. Based on these findings, this study proposes a Humanizing Data Science Framework integrating domain expertise throughout the AI lifecycle. The framework demonstrates that combining tech nical capabilities with human knowledge supports transparent, fair, accountable, trustworthy, and human-centered AI, providing valuable practical and theoretical guidance for researchers, practitioners, organizations, policymakers, and future interdisciplinary innovation initiatives worldwide.

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