A Practice Auditing Framework for Large Language Model Use: Collective Empiricism, Pseudo-Rational Cognition, and Governance of AI-Generated Content
arXiv:2607.01248v1 Announce Type: cross Abstract: Large language models are increasingly used for knowledge acquisition, code generation, academic writing, and agent-based automation. In these settings, users may obtain highly structured answers, plans, and judgments without sufficient domain practice. This paper proposes a practice auditing framework for LLM use and AI-generated content governance. It introduces collective empiricism to describe how LLMs compress and reorganize large-scale human experience into outputs that appear empirical and rational, and pseudo-rational cognition to descr
As LLMs become ubiquitous across critical applications, the need for robust governance and auditing frameworks for AI-generated content is becoming urgent.
This framework addresses the growing challenge of ensuring reliability and accountability in AI-driven knowledge acquisition and automation, fundamentally impacting trust in AI systems.
The proposed 'practice auditing framework' and concepts like 'collective empiricism' and 'pseudo-rational cognition' provide new tools for evaluating and governing AI output.
- · AI governance bodies
- · Auditors
- · Regulators
- · Companies implementing AI responsibly
- · Unregulated AI content producers
- · Users over-reliant on unverified LLM output
- · Entities resistant to AI scrutiny
Companies and organizations will begin to implement formal auditing processes for their critical LLM applications.
New standards and certifications for 'AI-generated content governance' will emerge, influencing procurement and deployment decisions.
Public and legal challenges regarding the veracity and origin of AI-generated content may shift liability structures, akin to traditional intellectual property or professional negligence.
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Read at arXiv cs.AI