From Objects to Influences: Rethinking Deletion in Learning-Based Systems
Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems 2026
Machine UnlearningDeletionHCIAI Ethics
Abstract
Deletion is a foundational operation in digital systems, yet its meaning has shifted as computing infrastructure has evolved. In non-learning-based systems, deletion targets identifiable data objects; in learning-based systems, data is transformed into learned influences distributed across model parameters, fundamentally changing what deletion can accomplish. Despite this shift, interfaces continue to present deletion as a familiar object-removal operation, leaving users to rely on outdated assumptions. Drawing on prior HCI literature, we synthesize the recurring assumptions users bring to deletion—Control, Scope, Completeness, and Transparency—and show that each is placed under structural strain when deletion shifts from object removal to influence mitigation. This mismatch between user expectations and system behavior reveals a critical gap that technical approaches alone cannot address. By framing deletion as an interactional concern, this work positions it as a critical HCI problem and outlines a user-centered research agenda for rethinking deletion in the context of learned influence.