arXiv:2605.28647v1 Announce Type: new Abstract: It is well known that LLM guardrails and trained persona dynamics can produce a reality gap: the distance between the world a LLM is permitted or shaped to describe, and the world in which users must act. Here we argue that actively generating reality gaps is in fact unethical because it knowingly shifts epistemic risk back to the uninformed user -- this is reality laundering. This can potentially cause harm when operationalised at scale. The risk is sharpest in high-exposure advice contexts, where users seek orientation rather than a bounded, ex
The increasing sophistication and widespread deployment of large language models, coupled with growing public and regulatory scrutiny, make the ethical implications of their operational dynamics highly salient.
This paper highlights a critical and under-addressed ethical risk in AI deployment, specifically 'reality laundering,' which shifts epistemic risk to users and can cause harm at scale, especially in sensitive contexts.
The explicit framing of LLM guardrails causing 'reality gaps' as unethical 'reality laundering' provides a new lens for evaluating AI development and deployment, potentially leading to increased regulatory pressure and design changes.
- · Ethical AI frameworks
- · Independent AI auditors
- · Users seeking transparent AI interactions
- · LLM developers prioritizing safety-ism over truth
- · Platforms deploying uncritical LLM experiences
- · Uninformed AI users
Increased focus on transparent AI models and explainable guardrail mechanisms will become a priority for developers and regulators.
New AI safety standards might emerge that specifically address the ethical implications of 'reality gaps' and emphasize epistemic responsibility.
The concept of 'reality laundering' could fuel public distrust in AI, leading to slower adoption or demands for human oversight in critical AI applications.
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Read at arXiv cs.AI