The DALOSS project advances understanding of data loss from purely technical problem to complex governance mechanism. This reframing enables new approaches to digital preservation, platform regulation, and information rights that account for political dimensions of technological systems.
Key innovations include developing research methods for studying closed digital platforms, creating computational techniques for systematic web loss analysis, and establishing theoretical frameworks connecting data loss across institutional contexts. The interdisciplinary approach bridges computer science, archival studies, and critical social theory revealing previously invisible aspects of digital governance.
Machine unlearning research provides crucial insights for AI regulation and ethics. As AI systems become central to social decision-making, understanding how they selectively "forget" information affects accountability, bias correction, and rights to be forgotten. Findings that unlearning operates through attention rather than deletion suggest current regulatory approaches may be insufficient.
Web archive research establishes foundations for systematic analysis of digital cultural heritage preservation. Developed methods enable comparative analysis of how different preservation approaches affect what gets saved for future generations, supporting evidence-based digital preservation policy.
The analytical framework provides tools for researchers, policymakers, and technologists across diverse digital contexts. By organizing data loss analysis around interfaces, ecologies, and storage, the framework enables systematic investigation of deletion politics from user experience through institutional practices to material infrastructures.
Future research includes scaling web loss analysis to comprehensive international datasets, extending AI forgetting research to emerging language models, and developing community-centered approaches to digital preservation. Policy applications include informing platform regulation accounting for deletion alongside data collection and developing institutional practices that democratize decisions about digital memory.
The collaborative approach demonstrates how academic research can contribute to preservation communities while advancing scientific knowledge, creating sustainable knowledge exchange supporting both research excellence and practical digital heritage preservation.
