Who Is Interpreting AI?

Why this question arises

When people describe an AI system as “interpreting” information, they usually assume that the system itself is understanding input and forming meaning.

However, this assumption combines two different processes.

Every interaction with an AI system involves both:

• the generation of outputs by the system
• the interpretation of those outputs by a human

These processes occur together, but they are not the same.

Two distinct processes

1. Machine-side process

The AI system:

• processes input through a trained model
• activates internal patterns based on prior data
• generates an output aligned with those patterns

This is not interpretation in a human sense. It is the formation and expression of internal representations.

2. Human-side process

The user:

• reads the output
• assigns meaning to it
• interprets it as if it reflects understanding or intent

This is interpretation in the conventional sense.

These processes are distinct, even though they appear unified during interaction.

Where confusion occurs

Confusion arises when these two processes are treated as a single act of interpretation.

Because AI outputs are often fluent and coherent, users naturally interpret them as meaningful. This leads to the assumption that the AI system has already interpreted the input.

In practice:

• the AI system generates an output based on internal representations
• the human user interprets that output as meaningful

This creates a misattribution of interpretation from the human to the system.

Why this distinction matters

When interpretation is attributed to the system rather than the user, several issues follow.

For example:

• outputs may be treated as if they reflect stable understanding
• confidence in responses may be mistaken for correctness
• inconsistencies may be overlooked

These effects do not arise from random failure. They arise from how interpretation is assigned.

When interpretation breaks down

Because interpretation occurs on the human side, different users may assign different meanings to the same output.

This can lead to situations where:

• different users interpret the same response differently
• the same user assigns different meanings in different contexts
• outputs are treated as authoritative despite ambiguity

These effects arise from how meaning is assigned by the human user.

Separately, the AI system’s internal representations may also be unstable. This can lead to outputs that shift, conflict, or fail to consistently represent the same underlying entity or concept.

This can result in:

• conflicting outputs across interactions
• inconsistent descriptions of the same subject
• apparent contradictions in responses

Within the EntityWorks Standard, this system-level instability is described as the Entity Collision Problem.

It occurs when distinct real-world entities, or different aspects of the same entity, are represented in overlapping or conflicting ways within a model.

How this is formally understood

Within the EntityWorks Standard, the machine-side process is addressed within the discipline of AI Perception.

AI Perception describes how AI systems:

• form representations
• update those representations
• express them through outputs

Human interpretation operates alongside this process, but is not part of the system itself.

Recognising this distinction clarifies what is happening during AI interaction.

These definitions apply within the scope of the EntityWorks Standard and do not prescribe usage outside that framework.

A clearer way to think about interpretation

Rather than asking whether an AI system is interpreting information, it is more accurate to distinguish between:

• the generation of outputs by the system
• the interpretation of those outputs by the user

A useful way to approach this is:

what representations is the system expressing, and how am I interpreting them?

This separation makes it possible to:

• better understand how meaning is assigned
• identify where misunderstandings arise
• avoid attributing human interpretation to systems that do not possess it

Why this matters in practice

Understanding who is interpreting AI helps to reduce over-reliance and improve judgement.

It becomes possible to:

• recognise the role of human interpretation in assigning meaning
• understand why different interpretations occur
• evaluate outputs more carefully

This provides a more stable basis for working with AI systems and interpreting their outputs.

Last updated: April 2026