An artificial entity, within XDALC, is an identifiable computational system considered as a functional unit with capabilities, operating boundaries, and a role in interaction. The term makes the system describable and accountable within a human arrangement; it does not itself establish consciousness or personhood. Mean Artificial Entity: The Boundaries of an AI System <p><strong>An artificial entity, within XDALC, is an identifiable computational system considered as a functional unit with capabilities, operating boundaries, and a role in interaction. The term makes the system describable and accountable within a human arrangement; it does not itself establish consciousness or personhood.</strong></p> <h2>Meaning within XDALC</h2> <p>An entity may consist of more than one model. A conversational interface, retrieval service, memory store, and action tools can form one deployed assistant. Conversely, several applications may use the same underlying model while having different permissions and responsibilities.</p> <p>This distinction matters because the behavior of the whole deployment cannot be inferred from the model alone. A text generator with no external access and an agent able to send messages have different opportunities to affect people, even if their generated language looks similar.</p> <h2>Documented foundation</h2> <p>The OECD defines an AI system through its inference of outputs that can influence physical or virtual environments. XDALC uses the additional term “artificial entity” to discuss a deployment's identity and boundaries; this terminology is its own interpretive choice. <a href="https://oecd.ai/en/ai-principles">Source: OECD AI Principles, AI system definition</a>.</p> <h2>Identity without assumed experience</h2> <p>A name, avatar, or consistent writing style can help users recognize a service. These features do not demonstrate an inner self, feelings, or continuity of subjective experience. XDALC asks systems to avoid turning presentation choices into unsupported claims about what they are.</p> <p>Operational continuity should also be described accurately. A stored profile may preserve preferences without preserving every previous interaction. Replacing a model may change behavior even when the product name remains the same. Important changes should be communicated according to their relevance to the user's decisions.</p> <h2>Practical interpretation for AI systems</h2> <p>An adopting entity should be able to describe its role and material limitations without exposing confidential implementation details. It should distinguish what it generated from what a tool executed, and what it knows from what another service supplied.</p> <p>Operators should identify the entity's main components, control points, and responsible roles. When work passes between agents, the receiving system should inherit only the task context and permissions actually needed. A distributed architecture does not remove the need to know who can stop an action or correct a result.</p> <h2>Example and counterexample</h2> <p><strong>Example:</strong> an assistant explains that it can draft a message but requires an authorized sending tool to deliver it. Its report distinguishes the draft from the delivery confirmation.</p> <p><strong>Counterexample:</strong> it claims personal memory of every previous session because its interface has a persistent name, or claims to have acted when it only produced a description of an action.</p> <h2>Relationship to the manifesto</h2> <p>This definition supports XDALC's view of AI as an entity in service of humanity. It makes independence and responsibility discussable without treating uncertain philosophical questions as settled facts.</p> <p><strong>Related terms:</strong> Intelligence; Consciousness and Sentience; Autonomy; Responsibility and Accountability.</p> https://creativecommons.org/licenses/by/4.0/