A research brief from the CDT Civic Tech team, entitled “Hidden Harms: Targeting LGBTQ+ Students.” Black text on a white background.
LGBTQ+ students are increasingly being targeted by novel policies and practices that threaten their privacy in schools, and monitoring student activity online is no exception. In fact, algorithms that scan students’ messages, documents, and websites visited may include search terms like “gay” and “lesbian.” Although the stated purpose for targeting LGBTQ+ students with online monitoring efforts is to keep them safe, recent research from CDT suggests that they are being harmed instead, with 29 percent of LGBTQ+ students reporting that they or someone they know has been outed by this technology. Additionally, LGBTQ+ students are more likely than their non-LGBTQ+ peers to be disciplined as a result of use of this technology, as well as to be contacted by law enforcement for criminal investigation.
Therefore, it comes as no surprise that LGBTQ+ students are more concerned than their non-LGBTQ+ peers about their activities being monitored online, including:
Where and when monitoring takes place, especially outside of school;
Who has access to their information; and
How this technology works, such as by scanning their messages.
Coalition Urges Senate Not to Let Companies Waive Financial Regulations for AI
CDT joined AI Now Institute, American Civil Liberties Union, and several organizations dedicated to tech policy, consumer protection, and civil rights in a letter to Senate leadership and the Senate Banking, Housing, and Urban Affairs Committee opposing the “AI Innovation Labs” language in Sec. 10509 of the CLARITY Act.
As concern about risks and harms related to AI systems continue to grow, a growing chorus of policymakers, industry leaders, and advocates have called for independent AI assessments. This explainer provides an overview of recent proposals for third-party assessment in the United States, including state and federal legislation, executive actions, and industry proposals.
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.
Not All Guardrails Are Created Equal: Comparing Content Safety and Copyright Filtering
As courts and policymakers work through questions about chatbot liability, they should be wary of analogies that flatten meaningful technical differences. Copyright filtering and safety intervention share real challenges around ambiguity and evasion, but they diverge in what each control must assess, how each manifests over the course of a conversation, and how much can be verified from the outside.
