Interpretable Machine Learning Frameworks for Ethical AI Deployment

Abstract

Interpretable machine learning (IML) has emerged as a critical enabler of ethical AI deployment, providing the technical mechanisms through which the opacity of complex models can be reconciled with the transparency, accountability, and non-discrimination obligations mandated by contemporary AI governance frameworks. Despite a proliferation of IML methods -- including inherently interpretable models, post-hoc explanation techniques, and hybrid architectures -- no consensus framework exists for selecting, integrating, and evaluating IML methods in the context of ethical AI deployment requirements. This paper proposes the Interpretable Machine Learning Deployment Framework (IMLDF), a structured methodology for aligning IML method selection with domain-specific ethical obligations, stakeholder explanation needs, and regulatory compliance criteria. The IMLDF is evaluated through a mixed-methods study combining a Delphi expert consensus process (n = 38 ML practitioners and ethics researchers across twelve countries) and a retrospective audit of 32 deployed AI systems across healthcare, financial services, criminal justice, and education domains. Expert consensus confirms strong agreement on IMLDF principle importance (Kendall W = 0.79, p < 0.001). Retrospective audit results demonstrate that IMLDF-aligned deployments exhibit a 46.1% lower rate of post-deployment ethics incidents involving transparency failures compared to non-aligned deployments (IRR = 0.54, 95% CI: 0.43-0.68, p < 0.001). The paper contributes the IMLDF specification, a validated IML-ethics alignment matrix, and a regulatory mapping tool for EU AI Act and GDPR compliance.