ChatGPT as the Marketplace of Ideas: Should Truth-Seeking Be the Goal of AI Content Governance?

TL;DR

This paper explores how ChatGPT embodies the marketplace of ideas metaphor, discusses the implications for AI content governance, and advocates for a knowledge-based approach emphasizing diverse viewpoints over truth-seeking.

Contribution

It demonstrates that ChatGPT effectively exemplifies the marketplace of ideas and proposes a shift from truth-seeking to a knowledge-based model for AI content governance.

Findings

ChatGPT shares key features with the marketplace of ideas theory.

Absolute truth-seeking in AI is unattainable and undesirable.

Risk management should involve multiple social actors, not just AI companies.

Abstract

As one of the most enduring metaphors within legal discourse, the marketplace of ideas has wielded considerable influence over the jurisprudential landscape for decades. A century after the inception of this theory, ChatGPT emerged as a revolutionary technological advancement in the twenty-first century. This research finds that ChatGPT effectively manifests the marketplace metaphor. It not only instantiates the promises envisaged by generations of legal scholars but also lays bare the perils discerned through sustained academic critique. Specifically, the workings of ChatGPT and the marketplace of ideas theory exhibit at least four common features: arena, means, objectives, and flaws. These shared attributes are sufficient to render ChatGPT historically the most qualified engine for actualizing the marketplace of ideas theory. The comparison of the marketplace theory and ChatGPT merely marks a starting point. A more meaningful undertaking entails reevaluating and reframing both internal and external AI policies by referring to the accumulated experience, insights, and suggestions researchers have raised to fix the marketplace theory. Here, a pivotal issue is: should truth-seeking be set as the goal of AI content governance? Given the unattainability of the absolute truth-seeking goal, I argue against adopting zero-risk policies. Instead, a more judicious approach would be to embrace a knowledge-based alternative wherein large language models (LLMs) are trained to generate competing and divergent viewpoints based on sufficient justifications. This research also argues that so-called AI content risks are not created by AI companies but are inherent in the entire information ecosystem. Thus, the burden of managing these risks should be distributed among different social actors, rather than being solely shouldered by chatbot companies.