Digitizing the Dead: Understanding ‘Data Hauntings’ to Support Equitable Data Work with Human Remains
Authors
Paper Title
Digitizing the Dead: Understanding ‘Data Hauntings’ to Support Equitable Data Work with Human Remains
Publication Info
- Topic area: Ethical and technical challenges in digitizing human remains in museums and collections.
- Keywords: Digitization, human remains, hauntology, ethical data work, museums, contested heritage, classification systems, provenance, metadata, collaborative decision-making.
Background and Problem
- Problem / challenge: The digitization of human remains lacks broadly accepted ethical guidelines, creating uncertainties in data management, classification, and regulatory compliance. Existing digital systems often fail to accommodate the complexity of this work.
- Significance: Ethical digitization of human remains is crucial for enabling research, ensuring accountability, and addressing the historical injustices embedded in these collections.
- Motivation and related work: Prior studies have focused on the ethics of displaying human remains and the politics of public digital displays, but little attention has been given to the internal data work required for digitization. This paper addresses this gap by examining the sociotechnical challenges and proposing solutions.
Solution
- Proposed approach: Introduction of the concept of "data hauntings" and a hauntological framework to support equitable data work with human remains.
- Novelty:
- Conceptualization of "data hauntings" to describe the historical, regulatory, and technical specters shaping data work.
- Development of a descriptive taxonomy of data work with human remains.
- Identification of three key areas of ethical uncertainty and strategies to address them.
- Proposal of a hauntological framework with principles for designing and maintaining equitable digital systems.
- Procedure and key techniques:
- Conducted 23 semi-structured interviews across 14 organizations in 7 countries.
- Thematic analysis of qualitative data to identify patterns and challenges.
- Mapped strategies for collaborative decision-making and equitable data curation.
Results
- Concrete findings:
- Identified three key areas of ethical uncertainty: outdated classification systems, legal/regulatory compliance, and provenance documentation.
- Highlighted the sociotechnical complexity of digitizing human remains, including resource constraints and ill-fitting digital systems.
- Developed a taxonomy of data work steps, from creating records to managing access permissions and indigenous data sovereignty.
- Advantage over baselines: Provides a structured framework for addressing ethical and technical challenges, filling a gap in existing guidelines and practices.
- Experiments / evaluation:
- Empirical study based on interviews with museum professionals and software developers.
- Analysis of diverse digital systems, from Excel spreadsheets to proprietary CMS.
- Case studies illustrating challenges and strategies in data work.
- Limitations and future work:
- Limited focus on non-English-speaking and under-resourced institutions.
- Future work could explore broader comparative studies and emerging technologies like AI in data management.
Summary
This study examines the ethical and technical challenges of digitizing human remains in museums and collections, introducing the concept of "data hauntings" to describe the historical, regulatory, and technical specters shaping this work. Through interviews with professionals across 14 institutions, the authors identify three key areas of ethical uncertainty—classification systems, regulatory compliance, and provenance—and propose a hauntological framework for equitable data work. The framework emphasizes historical traceability, ethical accountability, and searchability, offering actionable recommendations for policy, organizational change, and digital system design. This work provides a foundation for addressing the complexities of contested heritage and advancing justice-oriented data practices.
Research Questions / Practical Problems
Question signals indexed for this paper.
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Based on Jaccard similarity of research subtopics & professions (≥60%)