We often hear about making AI responsible—but what if the real problem isn’t the model? What if it starts much earlier, with the data we feed into it?

In this episode, we unpack the hidden assumptions baked into the way data is measured, labeled, and used. We explore why objectivity in data is often a myth—and why designing for subjectivity is essential for building trustworthy AI. From mental health dashboards and inconsistent clinical definitions to the neuroscience of neuron behavior and brain simulations, we examine how context—not just content—shapes what AI learns.

We introduce the FAIR² framework: a practical approach to making data not only Findable, Accessible, Interoperable, and Reusable—but also AI-ready, Responsible, and Context-Rich. This isn’t just about fixing bias. It’s about understanding how data is made—and building integrity into the foundation of every AI system.

If you care about scientific rigor, equity in AI, or reproducible research, this episode will shift how you think about data.

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