AI记忆永久留存,人类却会遗忘,这导致关系失衡。
Memory Power Asymmetry in Human-AI Relationships: Preserving Mutual Forgetting in the Digital Age
- AI能无限保存人类互动记录,人类则自然遗忘
- 四维记忆不对称机制使企业掌握关系主导权
- 提出设计原则,推动人与AI记忆权对等
随着人工智能深度嵌入个人与职业关系,一种由记忆能力不对称引发的新权力失衡浮现。人类关系长期依赖相互遗忘——双方自然淡忘细节——以维系心理安全、宽恕与身份变迁。而AI系统可大规模持久存储、检索并重组交互历史,常达永久。本文提出记忆权力不对称(MPA):当一方(通常为AI企业)显著具备记录、保留、调用和整合关系史的能力,并可单向利用这些信息,而另一方(人类)无法反制时,便产生结构性权力失衡。基于人类记忆、权力依赖理论、AI架构与消费者脆弱性研究,构建了包含持久性、准确性、可及性、整合性四个维度的框架,以及战略记忆部署、叙事控制、依赖不对称、脆弱性累积四种权力转化机制。推导出个体、关系/企业、社会层面的潜在后果,提出边界条件命题,并确立六项恢复记忆平衡的设计原则(如‘遗忘设计’、‘上下文隔离’、‘对称访问’)。分析表明MPA不同于信息不对称、隐私、监控或客户关系管理,主张保护相互遗忘,或至少实现对记忆的共同控制,应成为人工智能时代的核心设计与政策目标。
原文摘要 · Abstract (English)
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.
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