Purpose

The description of entities—whether legal entities (e.g., corporations, organizations) or natural entities (e.g., adults with legal personhood or dependents like children)—requires a structured framework to clarify their characteristics and associations with software agents. This ontology provides a standardized approach to describe entities and their interactions with agents, facilitating precise communication and alignment with legal, ethical, and societal frameworks.

Scope

The ontology applies to legal and natural entities and their associations with software agents, platforms, or systems, particularly in AI-driven contexts. It addresses entity characteristics, legal status, and agent-entity relationships, including ethical and responsibility considerations.

Audience

This document targets:

• AI researchers and developers
• Legal scholars and policymakers
• Industry stakeholders
• Ethics and human-computer interaction communities

Ambiguity in Entity-Agent Relationships

The lack of a standardized framework for describing entities (legal or natural) and their interactions with software agents leads to ambiguity in roles, responsibilities, and legal implications. For example, associating an agent with a child (dependent) versus an adult (legal person) requires distinct considerations for accountability and oversight.

Need for an Ontological Framework

An ontology provides a structured representation of entities and their relationships with agents, enabling clear classification and interoperability. This framework supports legal, ethical, and technical alignment in human-agent interactions.

Goals

• Define clear characteristics for legal and natural entities.
• Standardize associations between entities and software agents.
• Promote responsible, ethical, and legally compliant agent-entity interactions.

Definitions

Entity

A legal or natural entity, including those with legal personhood (e.g., adults, corporations) or dependents (e.g., children).

Legal Entity

An organization or institution recognized under law as having rights and responsibilities (e.g., corporations, governments).

Natural Entity

A human individual, either with legal personhood (e.g., adults) or as a dependent (e.g., children).

Software Agent

An autonomous or semi-autonomous computational entity capable of performing tasks or decision-making.

Ontology

A formal representation of knowledge as a set of concepts, properties, and relationships within a domain.

Related Concepts

Related terms include Human-Centered AI (HCAI), Human-Centric AI (HCA), and Responsible AI. Proposed acronyms for entity-agent associations:

• EAA: Entity-Agent Association
• LERA: Legal Entity-Responsible Agent
• NERA: Natural Entity-Responsible Agent

Scope Limitations

This ontology focuses on entities and their associations with AI-driven agents. Non-AI agents or entities without legal or natural status (e.g., abstract concepts) are out of scope.

Core Components

The ontology consists of:

• Classes: Categories of entities (e.g., Legal Entity, Natural Entity, Agent).
• Properties: Attributes describing entities and agents (e.g., Legal Status, Autonomy, Responsibility).
• Relationships: Interactions between entities and agents (e.g., Controls, DependsOn, Represents).

Taxonomy of Entity and Agent Characteristics

Relationships

Key relationships include:

• Entity-Agent: Controls, DependsOn, Represents, Augments.
• Entity-Entity: Governs, DependsOn, Benefits.
• Agent-System: IntegratesWith, Supports, AdaptsTo.

Proposed Acronyms and Terms

To standardize entity-agent associations:

• EAA: Entity-Agent Association (general interaction framework).
• LERA: Legal Entity-Responsible Agent (focus on legal accountability).
• NERA: Natural Entity-Responsible Agent (focus on human-agent symbiosis).
• Others: ECTA (Entity-Centric Technological Association), ERAI (Entity-Responsible AI).

Formal Representation

The ontology will be expressed in OWL (Web Ontology Language). Example structure:

        Class: Entity
          SubClasses: LegalEntity, NaturalEntity
          Properties: hasLegalStatus, hasCapacity
          Relationships: controls(Agent), dependsOn(Agent)
        Class: Agent
          Properties: hasAutonomy, hasTransparency
          Relationships: represents(Entity), augments(Entity)
      

Use Cases

• Healthcare: Associating a medical AI agent with a patient (natural entity, dependent or adult) or hospital (legal entity).
• Education: Linking adaptive learning agents to students (dependents) or teachers (adults with legal personhood).
• Legal Systems: Defining agent-entity interactions for corporate accountability in automated decision-making.

Example Annotations

System: AI-driven educational platform

• Entity: Student (Natural Entity, Dependent)
• Properties: Limited Capacity, Educational Context
• Agent: Adaptive Learning Agent
• Properties: High Adaptability, Medium Transparency
• Relationships: augments(Student), dependsOn(Teacher)

Benefits

• Clarity in defining entity roles and agent responsibilities.
• Enhanced legal and ethical alignment in agent-entity interactions.
• Support for standardized frameworks in AI governance.

Technical Implementation

• Use of semantic web technologies (RDF, OWL) for ontology encoding.
• Integration with legal and AI metadata standards (e.g., Dublin Core, ML Schema).

Adoption Strategies

• Collaboration with standards bodies (e.g., W3C, ISO, IEEE).
• Open-source ontology repository for community contributions.
• Training for developers, legal professionals, and policymakers.

Challenges

• Variability in legal definitions of entities across jurisdictions.
• Complexity of modeling dependent entities (e.g., children) in agent interactions.
• Need for continuous updates to reflect evolving legal and AI landscapes.