Memory Power Asymmetry in Human-AI Relationships: Preserving Mutual Forgetting in the Digital Age

TL;DR

This paper introduces Memory Power Asymmetry (MPA), a structural imbalance in human-AI relationships caused by AI's superior memory capabilities, and proposes design principles to restore mutual forgetting and balance.

Contribution

It conceptualizes MPA with four dimensions and mechanisms, and offers design principles to mitigate power imbalances in human-AI interactions.

Findings

Identifies four dimensions of MPA: persistence, accuracy, accessibility, integration.

Develops four mechanisms translating memory asymmetry into power.

Proposes six design principles for balancing memory in human-AI relationships.

Abstract

As artificial intelligence (AI) becomes embedded in personal and professional relationships, a new kind of power imbalance emerges from asymmetric memory capabilities. Human relationships have historically relied on mutual forgetting, the natural tendency for both parties to forget details over time, as a foundation for psychological safety, forgiveness, and identity change. By contrast, AI systems can record, store, and recombine interaction histories at scale, often indefinitely. We introduce Memory Power Asymmetry (MPA): a structural power imbalance that arises when one relationship partner (typically an AI-enabled firm) possesses a substantially superior capacity to record, retain, retrieve, and integrate the shared history of the relationship, and can selectively deploy that history in ways the other partner (the human) cannot. Drawing on research in human memory, power-dependence theory, AI architecture, and consumer vulnerability, we develop a conceptual framework with four dimensions of MPA (persistence, accuracy, accessibility, integration) and four mechanisms by which memory asymmetry is translated into power (strategic memory deployment, narrative control, dependence asymmetry, vulnerability accumulation). We theorize downstream consequences at individual, relational/firm, and societal levels, formulate boundary-conditioned propositions, and articulate six design principles for restoring a healthier balance of memory in human-AI relationships (e.g., forgetting by design, contextual containment, symmetric access to records). Our analysis positions MPA as a distinct construct relative to information asymmetry, privacy, surveillance, and customer relationship management, and argues that protecting mutual forgetting, or at least mutual control over memory, should become a central design and policy goal in the AI age.