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Realigning Algorithmic Bias shows what happens when systems finally stop improvising and start acting responsibly. This post highlights how repaired data replaces the bad inputs that warped predictions, how rewritten patterns correct the assumptions that misread users, and how adjusted motives shift platforms away from profit‑driven nudging. It shows how balanced inputs prevent the machine from treating difference like an error, how corrected outcomes retire nonsense recommendations, and how reb
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