Beyond the Algorithm
AI's Breakthroughs, Boundaries & Blind Spots

AI is often sold as smart, inevitable, and objective. In reality, it inherits the same assumptions, exclusions, and power structures already embedded in the data and institutions that shape it. Using women's health as a powerful and underexamined lens, this panel opens up a broader conversation about how AI affects all of us through trust, safety, accountability, work, decision-making, and who gets left behind.

We'll explore three key questions:

• Algorithmic Accountability: When AI makes a mistake, who is responsible: the doctor, the developer, or the company?
• The Invisible Patient: Why many AI tools work better for men than for women or people of color and how we need to move beyond binary diversity metrics toward community-led data ownership and intersectional design.
• Trust vs. Technology: When does prediction become surveillance and who bears the cost?

Bringing together perspectives from AI research, innovation, governance, and product, this panel will unpack how AI is built, from data and consent to bias, regulation, and accountability.

For anyone curious, skeptical, or simply trying to understand how AI shapes their life, this is a timely conversation about building responsible AI that prioritizes human rights, protects against gender and racial bias, and operates within a robust, transparent legal landscape.