Regulating Chatbot Output via Inter-Informational Competition
<|reference_start|>Regulating Chatbot Output via Inter-Informational Competition: The advent of ChatGPT has sparked over a year of regulatory frenzy. However, few existing studies have rigorously questioned the assumption that, if left unregulated, AI chatbot's output would inflict tangible, severe real harm on human affairs. Most researchers have overlooked the critical possibility that the information market itself can effectively mitigate these risks and, as a result, they tend to use regulatory tools to address the issue directly. This Article develops a yardstick for reevaluating both AI-related content risks and corresponding regulatory proposals by focusing on inter-informational competition among various outlets. The decades-long history of regulating information and communications technologies indicates that regulators tend to err too much on the side of caution and to put forward excessive regulatory measures when encountering the uncertainties brought about by new technologies. In fact, a trove of empirical evidence has demonstrated that market competition among information outlets can effectively mitigate most risks and that overreliance on regulation is not only unnecessary but detrimental, as well. This Article argues that sufficient competition among chatbots and other information outlets in the information marketplace can sufficiently mitigate and even resolve most content risks posed by generative AI technologies. This renders certain loudly advocated regulatory strategies, like mandatory prohibitions, licensure, curation of datasets, and notice-and-response regimes, truly unnecessary and even toxic to desirable competition and innovation throughout the AI industry. Ultimately, the ideas that I advance in this Article should pour some much-needed cold water on the regulatory frenzy over generative AI and steer the issue back to a rational track.<|reference_end|>
@article{zhang2024regulating,
    title={Regulating Chatbot Output via Inter-Informational Competition},
    author={Jiawei Zhang},
    journal={arXiv preprint arXiv:2403.11046},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11046},
    primaryClass={cs.CY cs.AI cs.ET cs.IT cs.LG math.IT}
}