What would machine learning look like if it was truly feminist? In this talk, we'll re-imagine what machine learning, particularly generative models, could and would look like if they were women+-led and proactively feminist, and how that diverges from today's models. Then, we'll look at ways that could drive community-driven machine learning, building systems with more trust, more diverse input and more empowerment at their core.
Katharine Jarmul is a privacy activist and data scientist whose work and research focuses on privacy and security in data science workflows. She recently authored Practical Data Privacy for O'Reilly and works as a Principal Data Scientist at Thoughtworks. Katharine has held numerous leadership and independent contributor roles at large companies and startups in the US and Germany -- implementing data processing and machine learning systems with privacy and security built in and developing forward-looking, privacy-first data strategy.
