CDT Comments on Protecting Privacy Rights and Ensuring Equitable Algorithmic Systems for Transgender and Gender Non-Conforming Students
The Center for Democracy & Technology submitted these comments to the U.S. Department of Education (ED) as part of ED’s review of Title IX, which protects students from discrimination on the basis of sex, gender identity, or sexual orientation. CDT urges ED to protect transgender and gender non-conforming students’ civil rights to privacy and the ethical, responsible use of their data.
In particular, ED should clarify that mandating the disclosure of a student’s transgender and gender-nonconforming status violates Title IX and also begin efforts to address the discriminatory effects of some algorithmic systems on lesbian, gay, bisexual, and transgender (LGBT) students.
CDT supports ED’s efforts to protect the rights of students based on their gender identity. We urge ED to adopt measures to protect student privacy, prevent discrimination, and ensure responsible, ethical data practices as an integral part of those efforts.
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.
