Algorithmic State Power And Constitutional Democracy: Reimagining The Rule Of Law In The Age Of Artificial Intelligence And Machine Learning

Abstract

AI and machine learning become more and more integral to the decision-making process within the public sector, important constitutional issues arise about transparency, explainability, accountability, fairness, privacy and procedural due process. The aim of this study is to fill the gap in the literature on systematically and interpretable ways to judge if algorithmic state power is in accordance with constitutional democracy and the rule of law. The goal is to create a quantitative, interpretable model for detecting and assessing constitutional risks in government algorithmic systems. The semantic representation of legal entities with Legal-BERT, the extraction of constitutional information and the implementation of a neuro-symbolic rule engine enable the analysis of records within the UK Algorithmic Transparency Recording Standard (ATRS) repository in the proposed Constitutional Neuro-Symbolic Risk Assessment (CNSRA) framework. The CARI is a metric that measures governance risks identified. The analysis reveals the following concerns with transparency (73.33%), explainability (70.00%), accountability (68.33%), due process (65.00%), fairness (60.00%) and privacy (56.67%). The overall CARI component score is 77.27% and the highest domain-level risk scores are found in law-enforcement applications (82.40%). The novelty of the approach is to combine legal language intelligence with the explicit constitutional reasoning and quantitative risk measurement. The framework outlines a readable and explainable audit framework of algorithmic state power, and concludes that constitutionally protecting rights is a requirement for a more transparent, accountable, rights-respecting and democratic approach to governance.