Episode 2: Who is at the AI table?

If AI is shaping our future, we have to ask: Who’s shaping AI? In this episode of The Tinker Table, Hannah digs into the essential question of representation in technology—and why it matters who gets invited to build the tools we all use. We explore how a lack of diversity in engineering and data science has led to real-world consequences: from facial recognition tools that misidentify women of color (Buolamwini & Gebru, MIT Media Lab, 2018) to healthcare algorithms that underestimated Black patients' needs by nearly 50% (Obermeyer et al., Science, 2019). This episode blends Hannah’s own research on belonging in engineering education with broader examples across healthcare, education, and AI development. You'll hear why representation isn’t just about race or gender—it’s about perspective, lived experience, and systemic change. And most importantly, we talk about what it means to build tech that truly works for everyone. Whether you’re a developer, educator, team leader, or thoughtful user—pull up a seat. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. Gender Shades: Intersectional accuracy Disparities in commercial gender classification. (2018). Proceedings of Machine Learning Research, 81, 1–15.