A Unified Multi-Objective Loss Function for Ethically Aligned Machine Learning

This technical whitepaper presents BiasHunter™, a modular audit framework for evaluating fairness, robustness, sparsity, and explainability in machine learning pipelines. The core contribution includes a mathematically grounded multi-objective loss function, KKT-based optimization under ethical constraints, and a deployable Python system validated on real-world datasets (COMPAS, Adult, CIFAR-10-C). The work is part of a broader initiative on ethically aligned AI development led by Seyed Arash Yousefi (IT&Science GmbH, Switzerland). Please READ the Actualized Paper Version HERE: https://zenodo.org/uploads/16630955