Memory Undone: Between Knowing and Not Knowing in Data Systems

2026-02-24

AI Summary

This work addresses the tension between data systems’ capacity to reinforce memory and their inability to support socially necessary forms of trustworthy “forgetting,” such as compliant data deletion, harm mitigation, and removal of harmful content. It reconceptualizes forgetting as a sociotechnical practice encompassing erasure, machine unlearning, and exclusion, highlighting its multidimensional nature across agency, temporality, reversibility, and scale. For the first time, forgetting is positioned as a first-class capability within knowledge infrastructures. Integrating machine unlearning, semantic data dependency management, participatory modeling, and large-scale manipulation analysis, the study proposes a governance framework centered on transparency, accountability, and epistemic justice. The analysis reveals that forgetting can be both empowering and silencing, offering a theoretical foundation for designing mechanisms that balance regulatory compliance, system utility, and fairness.

Technology Category

• Data Mining & Knowledge Management: Other Foundations of Data Mining & Knowledge Management
• Machine Learning: Privacy
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications

Abstract

Machine learning and data systems increasingly function as infrastructures of memory: they ingest, store, and operationalize traces of personal, political, and cultural life. Yet contemporary governance demands credible forms of forgetting, from GDPR-backed deletion to harm-mitigation and the removal of manipulative content, while technical infrastructures are optimized to retain, replicate, and reuse. This work argues that"forgetting"in computational systems cannot be reduced to a single operation (e.g., record deletion) and should instead be treated as a sociotechnical practice with distinct mechanisms and consequences. We clarify a vocabulary that separates erasure (removing or disabling access to data artifacts), unlearning (interventions that bound or remove a data point influence on learned parameters and outputs), exclusion (upstream non-collection and omission), and forgetting as an umbrella term spanning agency, temporality, reversibility, and scale. Building on examples from machine unlearning, semantic dependencies in data management, participatory data modeling, and manipulation at scale, we show how forgetting can simultaneously protect rights and enable silencing. We propose reframing unlearning as a first-class capability in knowledge infrastructures, evaluated not only by compliance or utility retention, but by its governance properties: transparency, accountability, and epistemic justice.

Problem

• forgetting
• data systems
• machine unlearning
• governance
• memory

Innovation

• machine unlearning
• sociotechnical forgetting
• data governance
• epistemic justice
• knowledge infrastructures