Dual erosion of equality and remedy in algorithmic decision systems

This article develops a dual erosion framework demonstrating how Artificial Intelligence systems simultaneously undermine equality rights through discriminatory outcomes and obstruct effective remedies through opacity. Through comparative case synthesis spanning criminal justice algorithms in the United States and Canada, welfare eligibility systems in the European Union (EU) and India, credit scoring in Germany, employment screening, and content moderation, the analysis reveals that equality risk and remedy risk operate interdependently rather than in parallel. Biased algorithmic outputs become unreviewable due to opacity, while opacity enables bias to persist undetected. The article examines regulatory responses under the EU Artificial Intelligence Act, the Digital Services Act, and the revised Santa Clara Principles, proposing concrete implementation frameworks that include discovery protocols reconciling trade secrets with due process, burden-shifting standards adapted from employment discrimination law, risk-tiered due diligence requirements, independent audit mechanisms, and participatory governance models. These findings advance algorithmic accountability scholarship by providing adjudicable standards for courts and regulators, while demonstrating that comprehensive governance requires integrated approaches that address both equality and remedy dimensions.