2025-09-13 –, Ballroom 3
AI systems increasingly make life-changing decisions about healthcare, employment, criminal justice, and financial services, but often perpetuate systemic discrimination against marginalized communities. Through real-world examples this talk examines how bias enters AI through biased data, algorithmic design, and deployment feedback loops. Attendees will learn a practical three-phase framework for building anti-oppressive AI systems and leave with concrete tools for identifying bias, implementing fairness metrics, and advocating for ethical AI practices in their organizations.
I am a machine learning leader, technologist, and mum who moved from Oregon to Melbourne in 2010 and never looked back. I started in academia with a PhD in ML (plus a side of ecoinformatics), wrangled data for environmental science, then shifted to industry for real-world impact.
I’ve led teams at Zendesk (hi, Answer Bot 👋), Culture Amp and now I’m Head of AI at Hatch, where we’re using data and design to help people find work they actually want. My work lives at the intersection of ethics, equity, and AI—and I care deeply about the long-term social impact of the systems we build. It's never “just tech”—it’s about power, access, and the messy human stuff behind the algorithms.
Along the way, I’ve juggled research, parenting, imposter syndrome, and the occasional chicken. What keeps me grounded? Gardening, gaming, and mentoring folks who don’t always see themselves in tech—yet.