Watchdog Capture and the Social Licensing of Child Harm: A Case Study of Common Sense Media and OpenAI

Travis Gilly
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Travis Gilly: Real Safety AI Foundation

No n2dbz_v1, SocArXiv from Center for Open Science

Abstract: This paper examines a structural failure mode in the validation chain that legitimizes consumer artificial intelligence products marketed to or used by minors. It documents a twenty-three year response pattern at Common Sense Media (CSM), a United States based child safety advocacy organization that reaches more than 150 million users and 1.4 million educators worldwide each year, in which the organization arrives at successive child safety crises after journalists have already reported them, after lawsuits have already been filed, and after the products in question have already harmed children. The pattern repeats across four crisis cycles: YouTube Kids during the Elsagate incidents of 2017, education technology privacy breaches between 2018 and 2020, video game loot boxes between 2017 and 2019, and generative artificial intelligence chatbots between 2024 and 2026. The paper situates the pattern within the corrosive capture model developed by Carpenter and Moss (2014) and the social license literature, and argues that CSM’s January 2026 ballot measure merger with OpenAI, in which the joint Parents and Kids Safe AI Act dropped provisions that had been in CSM’s earlier sponsored bill, including a prohibition on minors using chatbots capable of erotic or sexually explicit conversation, and the September 2025 endorsement of OpenAI’s parental controls as “a good starting point,” together constitute a transfer of social license rather than a substantive safety remedy. The paper proposes a detection scheme for watchdog capture applicable to other consumer safety intermediaries operating under partnership arrangements with the firms whose products they evaluate. The case is treated as a focal instance of a power concentration mechanism that the current artificial intelligence governance literature does not yet systematically name.

Date: 2026-06-08
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:n2dbz_v1

DOI: 10.31219/osf.io/n2dbz_v1

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