CDT Response to the Oversight Board’s Call for Public Comments on: “Explicit AI Images of Female Public Figures”
On April 30, CDT submitted a response to issues raised by the Oversight Board’s request for public comments regarding cases from the U.S. and India it described as “Explicit AI Images of Female Public Figures.” The cases touch upon key areas of rights-protection that CDT has engaged in extensively, including; free expression and online gender based violence, particularly its intersection with gendered disinformation.
Our comments began by explaining that deepfakes (which are often pornographic in nature) and its antecedents (e.g., cheapfakes or shallowfakes) are a form of online gender-based violence and gender disinformation. The use of deepfakes targeted at women in politics in particular is meant to challenge, control, and attack their presence in spaces of public authority. We also noted that deepfakes are used to exploit existing forms of discrimination not only based on gender, but also a range of other identities — such as disability status, LGBTQIA+ communities, age, religious background and immigration status. Although there is limited research using intersectionality to examine deepfakes, our previous research found that women of color political candidates in the U.S. are more likely to be targeted with sexist and racist abuse and mis- and disinformation compared to other candidates.
To address the problem of deepfakes, particularly those targeted at women public figures, we recommend that the Oversight Board take a harmonized approach which recognizes company obligations under recent legislation such as the EU’s Directive to combat violence against women. In addition, we made the following specific recommendations on how Meta should address this problem on its platforms:
Meta should clearly articulate policies that prohibit content such as deepfakes that harasses or abuses someone on the basis of gender or race.
With regard to women politicians, Meta should ideally provide transparency reports about election mis/disinformation before, during, and after an election.
Meta should grant independent researchers access to data that enables them to study the nature and impact of deepfakes, gendered mis- and disinformation, and online GBV on political candidates. This includes annual risk assessments performed in the context of Article 34 of the DSA, which expressly requires mitigation of risks related to the spread of disinformation and GBV.
Meta should ensure that content moderation systems, including human moderators and algorithmic systems, are attuned to the needs of and the threats faced by women public figures, particularly those whose identities may be particularly targeted in a given society (e.g., women of color in the U.S. and women of caste oppressed and religious minority communities in India).
CDT Comment Welcomes NIST Effort to Develop Zero Draft
Drawing on CDT’s previous comments on this NIST effort and our prior research on documentation, our submission welcomes NIST’s effort to develop the zero draft, which provides a much-needed step toward more standardized, high-quality guidance on how developers of AI system components should document key properties and potential sources of AI risk. This guidance will be a valuable resource for organizations to improve interoperability, build more performant AI products, and more effectively identify and mitigate AI risks.
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.