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The Subjectless Language of AI and the Fallacy of Perception: A Philosophical and Empirical Analysis of Societal Risks
Abstract
This paper examines the societal risks of the fallacy of perception, where humans misattribute meaning to the subjectless language of artificial intelligence (AI), potentially leading to systemic instability. It tests the proposition that pervasive AI adoption fosters trust erosion and institutional dysfunction through pseudo-subjectification, judgment dependency, and institutionalization. AI’s transformer-driven outputs, lacking intent, prompt projection of six meanings—cognitive, use-contextual, intentional, existential, relational, and logical. Integrating language philosophy (Wittgenstein, 1988; Austin, 1962), ontology (Heidegger, 1962), cognitive science (Turkle, 2011; Skitka et al., 1999), sociology (Latour, 2005), and ethics (Coeckelbergh, 2020), the study employs a 2025 survey (n=1,500, U.S., Japan, EU) and interviews (n=50) to quantify trust erosion (18–27%) and define instability thresholds (35%). Comparisons with advertising, religious narratives, and political rhetoric contextualize AI’s risks. The proposition is conditionally valid, mitigated by the EU AI Act (2025) and a 10% reduction in AI diagnostic errors (WHO, 2024), but persistent distrust (45% medical AI, X analysis, 2025) underscores urgency. Recommendations include standardized AI literacy curricula, algorithmic audits, and global ethical certification, advancing philosophy of technology and AI governance.
Keywords: Artificial Intelligence, Subjectless Language, Fallacy of Perception, Trust Erosion, Systemic Instability, Philosophy of Technology
1. Introduction
1.1 Background of the Problem
AI language models (e.g., ChatGPT, Grok) have transformed daily life, from restaurant recommendations to medical diagnostics, with the global AI market projected to exceed $300 billion by 2025 (Statista, 2025). However, their transformer-based outputs, statistical imitations of human speech, lack intent, experience, or responsibility, forming a subjectless language distinct from Wittgenstein’s (1988) language games or Austin’s (1962) speech acts. Humans, driven by cognitive structures, misinterpret outputs like “I recommend Restaurant A” as intentional advice, triggering actions like reservations. This fallacy of perception—misattributing meaning to the meaningless—distorts judgment and erodes trust.
The stakes are evident in real-world impacts. Social media algorithms amplified misinformation during the 2020 U.S. election, affecting 57% of users (MIT, 2021). A 2025 X post analysis reports a 45% rise in medical AI distrust due to misdiagnosis fears (X analysis, 2025). A 2025 survey (n=1,500, U.S., Japan, EU) conducted for this study found 65% of users attribute intent to AI outputs, with trust declining 18% post-error. Interviews (n=50) reveal users liken AI to “a deceptive salesperson” or “scripted preacher,” paralleling advertising and religious narratives. This crisis, rooted in AI’s subjectless language clashing with human meaning-making, risks systemic instability, necessitating philosophical and empirical scrutiny.
1.2 Research Purpose and Proposition
This study tests the proposition that pervasive AI adoption triggers a fallacy of perception, eroding trust and institutional integrity to risk systemic instability. It investigates how AI’s meaningless outputs prompt projection of six meanings, how these fallacies cascade into pseudo-subjectification, judgment dependency, and institutionalization, and how comparisons with advertising, religion, and political rhetoric illuminate risks. Using 2025 survey and interview data, alongside policy developments (EU AI Act), it evaluates the proposition’s validity and proposes countermeasures, contributing to philosophy of technology and AI ethics.
1.3 Theoretical Framework
The analysis integrates language philosophy (Wittgenstein, 1988; Austin, 1962; Bakhtin, 1981), ontology (Heidegger, 1962), cognitive science (Turkle, 2011; Skitka et al., 1999), sociology (Latour, 2005), and ethics (Coeckelbergh, 2020). Wittgenstein’s language games highlight AI’s meaninglessness, Austin’s speech acts critique its pseudo-utterances, and Bakhtin’s dialogism notes its relational absence. Heidegger frames AI’s non-existence, while cognitive biases (anthropomorphism, automation bias) explain meaning projection. Latour’s institutionalization and Coeckelbergh’s responsibility dilution contextualize risks. Comparisons with advertising (Schudson, 1984), religious narratives (Berger, 1967), and political rhetoric (Edelman, 1988) situate AI within pseudo-subject phenomena.
2. Methodology
The study combines philosophical reasoning, empirical data, and comparative analysis. It systematizes AI’s language generation and human meaning-making via six meaning categories (cognitive, use-contextual, intentional, existential, relational, logical), grounded in Wittgenstein, Austin, and Bakhtin. A technical analysis of transformer algorithms (Vaswani et al., 2017) clarifies meaning absence, while a logical proof (definitions, axioms, lemmas, theorems) tests the proposition. Case studies (restaurant recommendations, medical diagnostics, social media) illustrate impacts.
Empirical Data:
•Survey: A 2025 cross-national survey (n=1,500; U.S., Japan, EU; stratified by age, occupation) measures trust in AI outputs. Conducted via online panels, it reports 65% intent attribution, 18–27% trust erosion post-error, and 35% instability thresholds (logistic regression, p<0.01).
•Interviews: Semi-structured interviews (n=50, AI users in healthcare, education, media) explore perceptions, revealing analogies to advertising (“sales pitch”) and religion (“scripted dogma”).
•Secondary Data: EU AI Act (2025), X posts (45% medical AI distrust, 2025), WHO (10% diagnostic error reduction, 2024), UNESCO (60% AI grading adoption, 2024).
Comparative Analysis: Parallels with advertising (brand loyalty), religious narratives (textual devotion), and political rhetoric (symbolic manipulation) enhance generalizability. The analysis sequence—algorithm scrutiny, fallacy mapping, risk chain construction, proposition proof, countermeasure proposal—ensures rigor and falsifiability.
3. Discussion
3.1 AI Language Generation: Structural Imitation and Absence of Meaning
Transformer algorithms generate subjectless language (Axiom 1: AI lacks intent) by tokenizing inputs (e.g., “restaurant”), evaluating relationships via self-attention, and producing probabilistic outputs (e.g., “I recommend Restaurant A”) (Vaswani et al., 2017). Outputs integrate user data (e.g., preferences), collective data (e.g., 4.8 rating), and context (e.g., location), but lack sensory experience or intent. Reinforcement learning (RLHF) embeds designer biases (e.g., “ethical” criteria), yet AI remains non-intentional. The six meaning categories reveal AI mimics cognitive and use-contextual meanings but lacks intentional, existential, relational, and logical depth, akin to advertising slogans (Schudson, 1984) or religious texts (Berger, 1967).
3.2 Fallacy of Perception: The Integration of Meaning and Systemic Risks
Humans project meaning onto AI outputs (Axiom 2: Humans generate meaning), treating them as pseudo-subjects (Lemma 1). For example, AI’s “I recommend Restaurant A” prompts reservations, driven by Turkle’s (2011) anthropomorphism (AI as “friendly guide”), Skitka et al.’s (1999) automation bias (trust in 4.8 rating), personalization, and black-box opacity. The six meanings integrate: cognitive (denoting A), use-contextual (prompting booking), intentional (projected choice), existential (life significance), relational (social interaction), and logical (assumed rationality). The 2025 survey (n=1,500) finds 65% intent attribution, with 18% trust erosion post-error (p<0.01). Interviews liken AI to “a salesperson hiding motives.”
Healthcare and social media cases amplify risks. AI’s “no abnormality” output risks overtrust, with 45% distrust rise (X analysis, 2025). WHO (2024) notes 10% error reduction, yet 27% trust erosion occurs post-misdiagnosis (survey, 2025). Social media misinformation affected 57% of users in 2020 (MIT, 2021), with 68% distrusting AI after bias exposure (survey, 2025). Advertising’s brand trust and religion’s doctrinal faith parallel these fallacies, as does political rhetoric’s symbolic manipulation (Edelman, 1988), driving judgment dependency (Lemma 2).
3.3 Chain Reactions and Societal Risks
The fallacy shifts judgment to AI, diluting responsibility. The survey (2025) finds 58% of professionals defer to AI, with 20% trust erosion post-error. Black-box opacity obscures verification, echoing advertising’s manipulation or religion’s authority (Latour, 2005). Institutional adoption—60% AI grading (UNESCO, 2024), AI diagnoses (WHO, 2024), biased policing (X analysis, 2025)—standardizes hollow meaning (Lemma 3). Interviews highlight “loss of human agency” in AI-driven systems. The survey reports 72% distrust in AI governance, with 25% erosion post-bias.
At a 35% trust erosion threshold (survey, 2025; logistic regression, p<0.01), risks
