Research & Publications
My practical work in AI is built on a foundation of formal research. I focus on the core principles of what makes intelligent systems effective, transparent, and safe. My inquiry spans two main areas: modeling the cognitive growth of personalized AI and establishing architectural blueprints for ethical AI governance.
Below are the papers that detail my findings and propose new frameworks for building human-aligned systems.
Personalization in Memory-Enabled AI Systems
A Graph-Based Analysis of Evolving User Interaction Patterns
Abstract:
As artificial intelligence systems increasingly incorporate memory and personalization features, a new frontier emerges in understanding how these systems adapt to individual users over time. This research investigates the cognitive dynamics of personalization in memory-enabled AI, focusing on how functional behaviors—such as planning, contextual recall, and adaptive feedback—emerge and stabilize through repeated interactions. We propose a graph-based modeling approach to represent evolving assistant behaviors and their interconnections, allowing us to visualize the emergence of user-specific cognitive patterns. The findings aim to inform future AI architectures with enhanced interpretability, cognitive alignment, and ethical personalization mechanisms.
Key Contributions:
- A conceptual and technical framework for modeling personalization structurally, rather than as static metadata or profile values.
- A graph- and metrics-based approach for tracking the emergence, reinforcement, and adaptation of AI functions over time.
- A “cognitive zone” abstraction layer that links assistant behavior to symbolic roles (e.g., Planning, Feedback), enhancing interpretability.
- An empirical analysis of interaction logs that identified and measured recurring behavioral loops and patterns.
Exploring Societal Implications of Personalized, Memory-Enabled AI
A Reflective Study Through Human-AI Interaction
Abstract:
This essay examines how personalized, memory-enabled AI assistants can help without eroding autonomy. I define assistants as context-aware agents and argue that durable memory and continual learning enable useful adaptability—if governed. I synthesize prior work into a “coexistence compact” and an auditable governance schematic (gateway before write/read, why-cards, verification, audit log). I connect these mechanics to human–AI interaction benefits and risks (autonomy, manipulation, privacy) and propose evaluation cues for retention, transfer, and grounded responses. The result is a practical set of design patterns and policy pointers that make memory helpful, accountable, and within user control.
Key Contributions:
- Proposes an auditable “Governance Schematic” that models memory operations as first-class policy events.
- Defines a “Coexistence Compact,” a set of rules for user-controlled memory: scoped by default, minimized, expiring, and auditable.
- Introduces practical design patterns like “Policy-as-Code Gateways” to vet memory access and “Why-Cards” to explain an AI’s reasoning to the user.
- Outlines policy pointers for institutions to require memory transparency reports, standardize audit trails, and mandate audits of choice architecture.