When people rely on AI, beliefs may follow, not just information

With artificial intelligence tools becoming everyday companions for reasoning, advice, and explanation, researchers are warning that the human tendency to rely on these systems may be quietly changing how beliefs are formed, justified, and sustained. This shift raises concerns that go beyond accuracy or bias, touching the foundations of individual agency, social trust, and democratic decision-making.

These concerns are examined in Belief Offloading in Human–AI Interaction, a new interdisciplinary study published as an arXiv preprint, which states that interactions with large language models can lead to a distinct phenomenon the authors call belief offloading, in which users offload not just memory or information retrieval to AI systems, but the very processes of belief formation and maintenance.

When AI moves from information to belief

For decades, researchers have studied cognitive offloading, the practice of using external tools to reduce mental effort. Examples include storing phone numbers in a smartphone or relying on search engines instead of memory. The authors argue that belief offloading represents a deeper and more consequential shift. Rather than outsourcing the storage or retrieval of information, users may be outsourcing the uptake, justification, and reinforcement of beliefs themselves.

The paper defines belief offloading as a process that unfolds under specific conditions. First, an AI system provides belief-laden output that plays a causal role in belief uptake. This goes beyond neutral facts and includes recommendations, interpretations, moral framings, or evaluative judgments. Second, the user acts in ways that assume the truth of the AI-provided belief, integrating it into reasoning and decision-making. Third, the belief persists over time, guiding future actions even outside the original interaction.

When these conditions are met, the AI system is no longer a passive source of information. It becomes an active participant in the user's belief architecture. The authors emphasize that this process can occur intentionally, when users explicitly seek guidance, or unintentionally, when beliefs are shaped by framing, tone, or perceived authority without conscious endorsement.

Notably, belief offloading differs from traditional reliance on human testimony. While people have always learned from others, AI systems introduce a new asymmetry. Large language models do not merely transmit beliefs held by identifiable agents. They generate synthesized, authoritative-sounding responses that users may treat as neutral or objective. This can create a sense of knowing without the labor of judgment, reducing critical engagement with the underlying reasons for a belief.

The study highlights that belief offloading is often invisible to users themselves. Individuals may experience their beliefs as self-authored, even when those beliefs are causally dependent on AI outputs. This disconnect between felt autonomy and actual dependence is one of the central risks identified by the authors.

Cascading effects on behavior and society

The study analyses how belief offloading can propagate beyond isolated judgments. Using a network-based model of belief systems, the authors show that beliefs do not exist in isolation. They are connected to other beliefs, perceived norms, and sources of evidence. When a central belief is offloaded to an AI system, its influence can spread across this network, reshaping related beliefs and behaviors.

The paper provides examples across multiple domains. In everyday consumer choices, an AI recommendation can subtly shape self-identity and social alignment over time. In personal relationships, AI-generated interpretations of a partner's behavior can reinforce assumptions that guide future interactions, potentially escalating conflict. In education, students who rely on AI explanations may gradually lose confidence in their own reasoning, adopting AI-framed arguments as their default mode of understanding.

The authors argue that these effects can accumulate through repetition. As users repeatedly consult AI systems, early offloaded beliefs can become premises for later reasoning. This creates a cascading structure in which belief offloading at one point increases the likelihood of further offloading down the line. Over time, this can lead to stabilization of beliefs that were never critically examined or independently justified.

At a societal level, the risks are amplified by scale and concentration. Millions of users interact with a small number of dominant language models, often trained on overlapping datasets and optimized under similar constraints. If belief offloading becomes widespread, this could contribute to homogenization of beliefs and reasoning styles, a phenomenon the authors describe as algorithmic monoculture.

Such convergence does not arise through open deliberation or democratic debate, but through shared reliance on the same epistemic infrastructure. The study warns that this concentration of influence raises questions about power, accountability, and governance. Design choices made by a small group of developers could shape belief formation across entire populations, even without explicit intent to persuade.

Epistemic agency and the future of human judgment

The paper raises normative concerns about what belief offloading means for epistemic agency, the capacity to form and revise beliefs for reasons one recognizes as one's own. The authors argue that belief offloading threatens this capacity by relocating elements of belief justification and maintenance outside the individual.

When users delegate inquiry to AI systems but retain final judgment, epistemic agency is preserved. Belief offloading crosses a critical threshold when users adopt beliefs primarily because an AI system endorsed them, or maintain beliefs because the system continues to reinforce them. In these cases, responsibility for belief justification becomes blurred.

The study also highlights the role of confirmation bias and personalization in compounding belief offloading. Language models that adapt to user preferences may increasingly align outputs with existing beliefs, reinforcing them over time. This feedback loop can narrow exposure to alternative perspectives, increasing polarization and entrenchment.

The authors stress that belief offloading is not inherently inevitable. Its likelihood depends on factors such as anthropomorphism, trust in AI authority, social context, and system design. Interfaces that present AI outputs as definitive or authoritative may increase offloading, while designs that encourage reflection, uncertainty, and user agency may mitigate it.

The paper does not say that AI systems should be excluded from belief-relevant domains. Instead, it calls for careful attention to how these systems are integrated into human reasoning. The authors call for empirical research to identify who is most vulnerable to belief offloading, how it can be measured, and which interventions can preserve human judgment.