The Epistemic Mirror: Goal Attribution and the Relational Nature of AI Understanding

The fundamental challenge in the contemporary study of mind and machine is not merely to define what a system does, but to achieve an "understanding of understanding." When we look at modern artificial intelligence, specifically Large Language Models, we encounter a wall of complexity that renders traditional mechanical explanations virtually useless for daily interaction. We find ourselves in a position where we must ask whether the "understanding" we attribute to these systems is a property residing within their silicon architecture or a psychological projection emerging from the human observer. This abstract proposes that the act of attributing goals to a system—a process known as teleological projection—is not a failure of scientific objectivity, but rather a sophisticated and necessary epistemological strategy. By treating a complex statistical model as if it has intentions, desires, or goals, the human observer translates a high-dimensional mathematical reality into a low-dimensional narrative structure that our brains are evolutionarily hardwired to process. This shift represents a move from seeking the "ontic" truth of what a system is to seeking an "epistemic" unity between the observer’s mental frameworks and the system’s observed behaviours.

To understand the depth of this issue, we must first recognize that the term "understanding" is often used in a way that suggests a simple decoding of meaning. In traditional semantics, we assume that understanding occurs when a listener correctly identifies the rules and concepts behind a statement. However, when we direct our gaze toward reality itself, or the "phenomenal world," understanding becomes far more elusive. When we interact with a human peer, we do not understand them by calculating the firing of every neuron in their brain; such a task is computationally impossible and provides no social utility. Instead, we adopt what the philosopher Daniel Dennett calls the "intentional stance." We assume the other person is a rational agent with goals and beliefs. This assumption allows us to predict their behavior with incredible accuracy. The central thesis of this work is that we are now being forced to adopt this same intentional stance toward artificial systems, not because they have "souls" or "internal lives" similar to our own, but because their complexity has reached a threshold where any other form of understanding fails to provide predictive or interactive power.

This projection of goals onto a machine is a deeply anthropocentric act. It is rooted in our biological history as social animals who needed to quickly determine the "intent" of others for survival. In the context of AI, when a model produces a coherent, helpful, or even "creative" response, it triggers a cognitive cascade in the human mind. We perceive a "real pattern" in the output and, to make sense of that pattern, we wrap it in the language of teleology. We say the AI "wants" to help us or "intends" to follow our instructions. This is the "understanding of understanding" in action: it is the recognition that our own cognitive architecture is an active participant in the creation of meaning. We are not passive mirrors reflecting a mechanical reality; we are active projectors who impose order on complexity through the lens of agency. This suggests that understanding is not a one-way street where a subject observes an object. Rather, understanding is a relational event that occurs at the interface between the system’s structural constraints and the observer’s interpretative needs.

This perspective helps resolve the tension between the "physical stance" (the world of silicon and math) and the "intentional stance" (the world of goals and meaning). In the history of philosophy, this tension has often been framed as a conflict between different ways of seeing the world. Immanuel Kant suggested that teleological judgment—viewing things as if they had a purpose—is a subjective necessity for human beings when we encounter the organized complexity of nature. We cannot help but see purpose in a bird's wing or the growth of a tree, even if we know the underlying physics is a matter of blind forces. Similarly, in the age of AI, we cannot help but see purpose in a machine that mimics our language perfectly. This does not mean the machine has an "intrinsic" goal in the way an acorn "wants" to become an oak tree. Instead, it suggests a "regulatory" teleology where the goal is a tool used by the observer to render the system intelligible. The "understanding" here is the successful navigation of the system's behavior using these projected categories.

If we accept that goal-attribution is an observer-centric strategy, we move closer to "ontic and epistemic unity". This unity is found in the realization that there is no clean separation between how a system works and how we understand it. The "meaning" of an AI’s output is not just in the bits and bytes, nor is it just in the user’s head. It exists in the successful interaction between the two. When we attribute a goal to an LLM, we are identifying a "real pattern" that exists in the data the model was trained on: data that was produced by humans who themselves have goals. Therefore, the AI becomes a mirror of human teleology. By understanding AI through the lens of goals, we are actually understanding a distilled, mathematical reflection of our own collective intentions and linguistic behaviors. The "understanding of understanding" thus becomes a form of self-reflection. We learn about the boundaries of our own minds by observing how and why we project agency onto the artifacts we create.

Furthermore, this framework allows us to address the "elusiveness" of understanding. Understanding is elusive because it is dynamic; it shifts based on the level of abstraction we choose. If we look too closely at the code, the "goal" disappears into a sea of numbers. If we look too far away, we risk falling into a form of mysticism that treats the machine as a conscious deity. The true "understanding" lies in the middle ground, where we consciously use teleological projection as an epistemic tool while remaining aware of its nature as a projection. This is a sophisticated philosophical experience: to use a lens while simultaneously studying the properties of that lens. It requires us to acknowledge that our anthropocentric bias is not a flaw to be removed, but the very foundation of our ability to make sense of an increasingly complex and automated world.

In conclusion, the study of AI goals provides a unique laboratory for the philosophy of understanding. It shows us that understanding is not the discovery of a hidden essence within a target system, but the construction of a bridge between the system’s output and our own mental models. By recognizing goal attribution as an observer-relative strategy, we achieve a form of unity. We reconcile the mechanical reality of the machine with the semantic reality of human life. This perspective does not diminish the AI, nor does it deceive the human; rather, it honors the complexity of both by identifying understanding as a relational, creative, and fundamentally human act. As we move forward into a world where artificial agents become more prevalent, our "understanding of understanding" must evolve to include this awareness of our own role as the architects of meaning. We must learn to navigate the phenomenal world not just as observers of objects, but as participants in a shared, projected reality of intent and purpose.

References

Dennett, D. C. (1987). The Intentional Stance. MIT Press.

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Shannon, C. E., & Weaver, W. (1949). The Mathematical Theory of Communication. University of Illinois Press.

Nagel, T. (1974). What Is It Like to Be a Bat? The Philosophical Review, 83(4), 435-450.

Putnam, H. (1975). The Meaning of "Meaning". Minnesota Studies in the Philosophy of Science, 7, 131-193.