Evaluation of Ethical and Fairness Constraints in Algorithmic Decision Making Using Auditable Machine Learning Pipelines

Abstract

Algorithmic decision-making systems are increasingly deployed across critical sectors such as healthcare, finance, and criminal justice. However, these systems often operate as opaque black boxes, raising concerns around fairness, accountability, and transparency. This paper investigates the implementation of ethical and fairness constraints within auditable machine learning (ML) pipelines, focusing on how traceability, interpretability, and post hoc auditability can be systematized. We develop an experimental auditable pipeline framework that enforces fairness constraints and allows third-party evaluation. By simulating common fairness scenarios on benchmark datasets, we demonstrate that auditable pipelines enhance transparency and reduce disparate impact without sacrificing predictive performance. Our analysis provides insights for regulatory frameworks and industry guidelines aimed at responsible AI development.