Algorithms embed historical discrimination into automated systems. Credit scores, hiring algorithms, risk assessments - they learn from biased past data and perpetuate inequality at machine speed. Algorithmic redlining is efficient, scalable, and claims objectivity. Discrimination becomes infrastructure.