Historical Homologation in AI Algorithmic Computation: When the Past Decides Your Future

Abstract

This paper introduces “historical homologation”, the systematic tendency of algorithms to make future decisions match past patterns regardless of contemporary evidence. Analyzing the 2020 UK A-level grading controversy, where algorithms downgraded 40% of teacher assessments, we demonstrate how the Ofqual DCP algorithm was designed to protect grade distributions rather than predict individual achievement. Through analysis of 55,000 schools, we identify three core mechanisms. Historical anchoring transformed 2017-19 grade averages into computational rules functioning as hard ceilings. Individual erasure compressed all achievement data into class averages, systematically disadvantaging high-achievers in lower-performing schools. Temporal smoothing operated as a low-pass filter, pulling trajectories back toward historical means. These mechanisms interact synergistically to create computational determinism, the structural necessity that algorithms reproduce rather than transcend historical patterns. This reveals historical homologation as an ontological constraint where historically anchored algorithms shape social futures by overlooking the very changes and exceptions that systems should prioritize