Hardware-Optimized Explainable Machine Learning for Fair Predictive Decision Making in Business Applications

Abstract

To enhance the objectivity of decision-making on predicting business applications, the paper presents a hardware-optimized explainable machine learning framework. The model unites explainability and fairness policies and hardware-conscious optimization to mitigate the shortcomings of black-box and hardware-agnostic models. Examination of representative business data shows prediction accuracy of up to 87.9%, which translates to an increase of 14.8%- 17.3% compared to the baseline methods. Parity inequality falls below 21.6% because it guarantees that outcomes of program decisions are fairer across sensitive attributes. Moreover, the proposed framework obtains a reduction in inference latency of 18.9% and a reduction in energy consumption per decision cycle of 13.7%. The consistency of explainability is increased by 19.4% and offers consistent and trustworthy feature-level factualization. In general, the findings substantiate the idea that explainability, fairness, and hardware efficiency optimization, when combined, result in scalable, transparent, and high-performance predictive decision-making systems in practice in businesses.