The Limits of Human Perception in the Age of Artificial Intelligence and the Problem of Discerning Truth

Abstract

This study examines potential transformations in human apprehension of knowledge amid the proliferation of artificial intelligence systems, understood as technical media that process human data through algorithms for analysis and organization. It investigates the relationship between direct knowledge and knowledge reproduced through these systems, and its effect on the human capacity to distinguish original truth from mediated epistemic representation.

Introduction

The modern era has witnessed rapid development in artificial intelligence systems, making them a fundamental component of the production and circulation of knowledge. This transformation concerns not only faster access to information but also the way understanding itself is formed.

The study proceeds from the hypothesis that artificial intelligence may be not merely a tool for retrieving knowledge but an epistemic mediator that contributes to reshaping how human beings perceive reality.

Conceptual Framework

These systems rely on mathematical models within the fields of machine learning and deep learning, trained on vast quantities of human data to recognize patterns and generate linguistic or analytical responses.

The knowledge they produce is therefore not independent; rather, it is a reorganization of human knowledge within a predefined technical framework, subject to rules of design and operation established by human beings.

The Problem of Epistemic Mediation

As the use of these systems expands, a gradual transformation emerges in how knowledge is accessed, consisting in a human shift from direct interaction with sources to reliance on a technical mediator that filters and organizes information.

The problem can be summarized as follows: declining reliance on direct verification and personal experience; increasing reliance on ready-made, preprocessed results; the possibility of standardizing the presentation of knowledge through similar linguistic patterns; and difficulty distinguishing original knowledge from reproduced knowledge.

Knowledge of the Unseen and Linguistic Re-representation

These systems treat religious and metaphysical concepts as linguistic texts within human data. In this context, these concepts can be analyzed as linguistic units amenable to processing, as cultural and historical concepts, and as elements within multiple interpretive systems, without this entailing any apprehension of their doctrinal content or their content pertaining to the unseen, since the system's operation remains confined to linguistic representation and statistical analysis.

The Mechanism of Technical Regulation and Control

These systems operate within a framework of constraints defined by human beings. The rules originate with the developers and research institutions overseeing the system, while enforcement mechanisms include filtering data during training, human evaluation of outputs, and setting limits on the types of content permitted. Their mode of operation rests on data-dependent statistical learning models, without possessing consciousness, will, or the capacity to establish rules autonomously.

Potential Consequences of Epistemic Transformation

If increasing reliance on these systems continues, the following transformations may emerge: knowledge becoming a mediated product rather than a direct experience; an increasing role for technical systems in shaping public understanding; a redefinition of the concept of “verification” to include digital media; and a widening gap between lived reality and epistemic representation.

The Risks of Control over the Tool

Systematic manipulation of truth. Artificial intelligence can be employed to produce and disseminate seemingly credible disinformation on a large scale, with the aim of distorting or recasting reality.

Covert direction of collective perception. By controlling what is displayed and what is concealed, it is possible gradually to influence people's awareness and shape their opinions without their being directly aware of it.

Weakening the human capacity for discernment. With increasing reliance on the system as a primary source of information, critical verification skills may decline, and uncovering the truth may become more difficult.

Artificial intelligence possesses neither consciousness nor self-awareness, and what appears to be “understanding” is in fact the result of processing human data according to algorithms and rules established by human beings; it reflects not an awareness of its own but the knowledge and patterns it has learned from multiple human sources.

Conclusion

The study demonstrates that artificial intelligence constitutes a new epistemic mediator that relies on reorganizing human knowledge through analytical algorithms, potentially affecting how human beings perceive information and distinguish direct knowledge from reproduced knowledge.

As reliance on it expands, a problem emerges concerning the possible decline of individual verification and increasing reliance on technical media in shaping public understanding. The study also indicates that these systems possess no self-awareness but operate within data and rules established by human beings; nevertheless, their widespread use may reshape the relationship between human beings and truth in the future.