Model Policy and Infographic: Non-Consensual Intimate Imagery (NCII) for K-12 Schools
Graphic for CDT model policy and infographic, tackling Non-Consensual Intimate Imagery (NCII) for K-12 Schools. Illustration of phone scanning a young person’s face.
In 2024, the Center for Democracy & Technology (CDT) conducted surveys of public high school students and public middle and high school parents and teachers to understand the prevalence of deepfakes, NCII, and related issues in K-12 schools. These surveys showed that concerns over the widespread nature of NCII were well-founded, and that there were significant gaps in schools’ current capabilities to handle the issue. Namely, efforts to educate students about NCII, prevent the conduct, and provide support to those who have experienced NCII were sparse.
Although addressing NCII, both authentic and deepfake, will require a long-term, multistakeholder approach, CDT convened a taskforce of diverse stakeholders and advocates to create a Model NCII Policy for K-12 schools as a first step toward filling the gaps identified in our research. Specifically, the Model NCII Policy:
Outlines the offense of sharing NCII;
States the school’s role in circumstances where NCII has been shared by/of a member of the school community;
Promotes visibility of reporting mechanisms and supportive measures for those impacted by the sharing of NCII; and
Provides customizable language that schools/districts can choose to integrate the policy into existing Title IX, bullying/harassment, and/or digital citizenship policies.
CDT also created an infographic to prevent distribution of NCII and support its victims that includes tips and resources about deepfake NCII. The infographic is intended to be useful in a number of formats, whether as a poster, a hand-out, or shared in its digital format.
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Having third parties assess AI systems might seem like common sense, but crafting effective policies toward this goal can be devilishly tricky. A poorly-constructed ecosystem for third-party assessment could easily fail to consider the most consequential mechanisms of risk, neglect the AI harms that most impact people, or do more to protect AI companies than people.
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