Data Repair

2026-02-02

AI Summary

This study addresses the challenges confronting data curation practices in the Global South—including a scarcity of high-precision tools, insufficient cross-lingual resources, constrained local knowledge sharing, and postcolonial ethical tensions—through a six-month ethnographic fieldwork in Bangladesh, combining in-depth interviews and participant observation. It is the first to situate data curation within frameworks of global data justice and infrastructure studies, systematically analyzing how practitioners mobilize external resources such as large language models and international technical forums. The findings reveal that data curation pricing reflects not only technical labor but also embedded affective value, while exposing a structural tension between mechanisms for protecting local knowledge and dependencies on global technologies. This work offers a novel perspective on “data poverty” for human-computer interaction research and advances a more ethically attuned agenda for data justice.

Technology Category

• Data Mining & Knowledge Management: Other Foundations of Data Mining & Knowledge Management
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

Abstract

This paper investigates data repair practices through a six-month-long ethnographic study in Bangladesh. Our interviews and field observations with data repairers and related stakeholders found that, alongside the scarcity of high-precision machinery and access to advanced software, data repair work is constrained by cross-language learning resources and the protective nature of documenting, curating, and sharing the experiences and knowledge among local peers. Repairers turning to external resources such as foreign forums and LLMs also revealed their frustrating experiences and the postcolonial ethical tensions they encountered. We noted that both anticipated technical labor and the emotionality of data were taken into account for pricing the data repair job, which contributed to their market sustainability strategies. Engaging with repair, infrastructure, and data poverty discourse, we argue that data repair practices represent a crucial challenge and opportunity for HCI in advancing global efforts toward data equity.

Problem

• data repair
• data poverty
• postcolonial ethics
• knowledge sharing
• cross-language barriers

Innovation

• data repair
• ethnographic study
• postcolonial ethics
• data equity
• technical labor