Context Windows, Memory, and Consent: The Ethics of Persistent AI Context

The shift from stateless LLM interactions to persistent-memory AI systems creates a fundamentally new ethical surface. When an AI 'remembers' that a user is going through a divorce, has a chronic illness, or holds a particular political view, the power dynamics of the interaction change dramatically. Four ethical design principles apply. First, contextual integrity: information shared in one context (e.g., a vulnerable moment in a conversation) should not automatically flow into other contexts (e.g., informing pricing or content filtering). This principle, developed by philosopher Helen Nissenbaum, is now used in EU AI Act compliance frameworks. Second, reversible consent: users should be able to inspect, edit, and delete the AI's contextual memory at any time — and deletion should be functionally complete, not merely flagged. Third, transparency about influence: when context materially shapes an AI output, the system should surface this. 'I'm responding this way partly because of what you told me last week' is ethically superior to silent personalization. Fourth, manipulation safeguards: systems should be designed to avoid exploiting emotional or cognitive states detected from context — this requires explicit product constraints, not just good intentions from engineers. Legal frameworks like GDPR's right to explanation and the EU AI Act's transparency requirements for high-risk systems are beginning to encode these principles in law. Teams building persistent AI products should treat context ethics as a product requirement, not a policy concern.