Constitutional Adversarial Debiasing: A Fairness- Aware Framework for AI-Assisted Legal Decision- Making in Indian Courts

Is it possible for the more stringent mathematical optimisation for artificial intelligence to coexist with the complex and historically ambiguous provisions described in the Indian Constitution? Within the scope of this research, we provide a novel framework for Constitutional Adversary Debiasing (CAD), with the intention of accomplishing these particular objectives. Instead than being a surface-level or post-processing problem, we would like to see the principles presented in Articles 14 and 15 become a part of the discipline of geography. In order to guarantee that the algorithm's inherent biases are still discernible, this step is taken. A content delivery network (CDN) is the most important part of this system. Consider it an adversary from within: the content delivery network (CDN) actively fine-tunes everything until every discrimination tertium-non-datur through biassed norms equality. It even uses domain embeddings and multi-head attention to uncover subtle structures, while the main model makes an effort to prevent people from saying anything that might be legal. However, a model in the wild is inherently hazardous. To ensure that decisions are based on actual court precedent cases and remain grounded in reality, we implement a Legal Heuristic Control. It is an attempt to emulate the flexibility of common law rather than the code, which has always been in place. As usual, the raw data from Indian courts is a little sloppy. Two solutions to this issue are to use a Legal Text Normaliser to clean up the data in case papers before it reaches latent space and a Judicial Explainability Module to ensure that human judges can correctly interpret reports. What is novel is the viewpoint. We map constitutional principles using the latent space of concepts in our model. This suggests that mathematical vectors and legal values are no longer distinct concepts. A framework that employs an adaptive calibration system to reduce confidence scores in the presence of bias and is not reliant on a single model.