Year: 2025
Type: article
Abstract: The invisible hand of data engineering shapes social outcomes through seemingly technical decisions that disproportionately impact vulnerable populations. This article examines how data architecture choices, pipeline failures, and default values in automated decision-making systems create systemic barriers to financial inclusion. Drawing from evidence across global financial institutions, the text reveals how household relationship models disadvantage non-traditional families, recovery mechanisms exacerbate inequalities during system outages, and default assumptions penalize those with limited financial histories. The findings demonstrate that engineering decisions are not merely technical but represent consequential policy choices with significant equity implications. The article concludes by proposing interventions at technical, organizational, and professional levels to advance more socially responsible data engineering practices that can reduce disparities while maintaining system performance.
Source: European Modern Studies Journal
Author: Rahul Joshi
Institution: Indian Institute of Technology Kharagpur
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: diamond
OpenAlex ID: W4413939475