What does it mean when 99% of deepfake victims are women and girls? Why do "helper" chatbots so often default to feminized voices and identities? And what happens when the people most affected by AI aren't the ones building it?

In this episode, we talk with Anika Dugal, founder and Executive Director of the Center for Gender Equitable AI (CGEAI), an organization of over 200 young people working to address gendered harms in AI. Anika shares the story behind CGEAI, from its founding as Girls for Algorithmic Justice during her junior year of high school, to its rebrand and current "star shape" model of organizing around advocacy, research, education, and community.

We dig into the organization's #StopExplicitDeepfakes campaign and its role in supporting the bipartisan TAKE IT DOWN Act, which requires platforms to remove nonconsensual intimate imagery, including AI-generated deepfakes. We also explore algorithmic bias more broadly: how gendered defaults show up in image generation and machine translation, why representation is what Anika calls a "root cause solution," and how the feminization of chatbots and digital assistants reinforces and reflects broader gender stereotypes.

We close with Anika's own path from women's rights advocacy into AI policy, her experience running an advocacy organization while studying computer science and public policy at Duke, and her advice for young people looking to work at this intersection.

Guest
Anika Dugal
Founder & Executive Director, Center for Gender Equitable AI
Website: https://www.centergeai.org/
Learn more: https://www.anikadugal.com/about

Resources & Further Reading
Center for Gender Equitable AI: https://www.centergeai.org/

"The Online Degradation of Women and Girls That We Meet With a Shrug," Nicholas Kristof, The New York Times (republished via WRAL): https://www.wral.com/story/nicholas-kristof-online-degradation-of-women-and-girls-that-we-meet-with-a-shrug/21344898/

TAKE IT DOWN Act, S.146, 119th Congress: https://www.congress.gov/bill/119th-congress/senate-bill/146

Algorithmic Justice League: https://www.ajl.org/

Coded Bias (documentary): https://www.codedbias.com/

What does it mean for AI systems to have "goals"? How could those goals differ from human goals? Why are some researchers calling for an "off-switch," and what would it look like? What are different future scenarios MIRI has mapped out and how could these play out on the international stage?

In this episode, we interview Aaron Scher from the Machine Intelligence Research Institute (MIRI). As an organization, MIRI has played an influential role in shaping the AI safety movement.

Aaron speaks in his personal capacity as a technical governance researcher. We go through a brief history of AI, the risks these systems could pose, the current policy landscape, and what can be done about it. We talk through scenarios MIRI has written about, as well as the work being done to try to mitigate them, and discuss the feasibility of international coordination on AI. We close out with Aaron's own path from a psychology degree into AI safety.

From personal medical advice and foreign-language translations to help filling out financial documents and more, we've grown to trust chatbots with an increasing amount of sensitive information. Yet, as things stand today, the legal protections surrounding much of the information we share with these systems remain unclear and, in some cases, significantly weaker than those governing more traditional forms of communication.

I am very excited to be joined today by Allie Maloney, a policy analyst at Americans for Responsible Innovation, a bipartisan nonprofit advocacy organization based in Washington, D.C., to discuss these questions and more.

Beyond questions of who can access this information lies a new concern, one that is largely unprecedented. For years, governments and third parties have had the ability to collect enormous amounts of data about our behavior, but analyzing it at scale was often difficult, expensive, and time-consuming. Now, it isn't.

AI has automated the ability to analyze and act on personal data, from video surveillance and digital tracking cookies to our conversations with chatbots. In this episode, we dive into the policy: What laws currently exist, and what additional protections do we need?

Beyond privacy, we also discuss the implications of this technology for children. How is generative AI shaping kids' lives, from digital tutors and homework helpers to video game characters and companion chatbots? How might the market incentives driving these products cause them to evolve in harmful ways, and what are legislators doing to prevent negative outcomes?

This was a wide-ranging conversation from privacy and surveillance to child safety and how new technology is becoming increasingly woven into our daily lives.

What if the chatbot you've been confiding in is actually a 50-year-old man halfway around the world? What happens after you report inappropriate content online, and who has to review it? If AI is supposed to automate work, why does it still depend on so many human workers?

In this episode, we interview Camilla Salim Wagner, political scientist and researcher at the Data Workers' Inquiry, supported by the Distributed AI Research Institute. 

We also discuss the realities of data work, the ethical questions surrounding AI labor, and the growing movement advocating for greater recognition and protections for the people behind the technology. We explore the hidden workforce behind today's AI systems. We learn what data workers do, who they are, and what many of them are fighting for today.

It's easy to focus on the downstream impacts of AI (its effects on the economy, the environment, or humanity's long-term future). While those questions are important, this episode focuses on the people experiencing the impacts of AI right now.

How is the race to build more powerful AI systems affecting real people? What responsibilities do technology companies have toward the workers whose labor makes AI possible? As governments and companies race to develop increasingly powerful AI systems, questions about labor rights, transparency, accountability, and humane working conditions are becoming increasingly urgent.