Continuity and Accountability in Human-Centered AI Systems

Abstract & Introduction

AIA Orbis Research Division

Abstract: The transition from content-centric to interaction-centric digital systems introduces structural requirements that conventional artificial intelligence architectures are not designed to satisfy. In environments where relevance, trust, and authority emerge through continuity of interaction rather than episodic content delivery, the absence of persistent identity and accountability mechanisms becomes a limiting factor.

1. Introduction: Digital interaction systems increasingly function as continuous conversational environments rather than static information channels. Users engage through persistent dialogue, expect contextual awareness across sessions, and infer trust from consistency of behavior rather than from isolated responses. While contemporary AI systems excel at generating contextually relevant outputs, they lack intrinsic mechanisms for long-term identity continuity and responsibility attribution.

Core Architectural Principles

2. Continuity as an Architectural Requirement: Continuity in interaction-centric systems refers to the preservation of identity, intent, and contextual memory across time, channels, and modalities. In human systems, continuity is implicit. In artificial systems, it must be explicitly engineered.

3. Accountability in Human–AI Systems: Accountability requires that actions, decisions, and representations remain attributable to a responsible entity. An accountability-capable system therefore requires identity binding, memory provenance, and auditable decision traces as foundational components.

Framework Abstractions: ASAI & HEIS

4. ASAI and HEIS as System Abstractions: AnthroSentient AI (ASAI) represents a human-centered intelligence framework in which identity, memory, emotion, and judgment are treated as first-class system primitives. Human Emotional Identity Structure (HEIS) operationalizes this framework by encoding identity signals and interaction context into structured, persistent representations.

5. Identity Binding and Memory Provenance: Identity binding ensures that all interaction artifacts are persistently associated with a single human entity. Memory provenance mechanisms maintain traceability of stored and retrieved information, preventing misattribution and unauthorized inference.

Risk Management & Evaluation

6. Failure Modes and Risk Considerations: Primary failure modes include identity collision, memory misattribution, hallucinated continuity, and over-inference of sensitive traits. Mitigation strategies focus on conservative attribution thresholds and verifiable audit trails.

7. Evaluation Methodology: Evaluation relies on metrics such as identity resolution accuracy, memory attribution precision, and completeness of audit records assessed under both nominal and adversarial conditions.

8. System Implications: Continuity- and accountability-aware architectures enable scalable human participation in interaction-dense environments without identity substitution.

10. Conclusion: Continuity and accountability constitute structural requirements for human-centered AI systems operating in interaction-centric environments. Within the AIA Orbis research framework, ASAI and HEIS represent a foundational response to these requirements.