Ethical AI Frameworks For Governance Of Intelligent Systems

Abstract

The rapid adoption of intelligent systems in data-driven domains has amplified concerns regarding transparency, accountability, and governance in artificial intelligence (AI). Existing investigations often address either predictive performance or ethical compliance independently, resulting in limited integration between intelligent decision-making and responsible AI governance. This research proposes a Deep Learning (DL)–based Ethical AI process for governing intelligent systems. that combines advanced DL with structured ethical governance mechanisms. The Environmental, Social, and Governance (ESG) Financial Governance Dataset contains 4000 records and 14 columns describing financial performance indicators, ESG metrics, governance attributes, risk factors, and sustainability-related variables. The proposed method integrates a Sine Cosine Algorithm-fused Efficient Long Short-Term Memory (SCA-ELSTM) model for deep feature learning and ESG risk prediction; SCA used to tunes the model parameters to improve prediction accuracy, convergence speed, and overall learning performance. ELSTM is used for deep feature learning and temporal dependency extraction, an Ethical Governance and Transparency Analytics (EGTA) for ethical risk assessment, governance compliance monitoring, transparency evaluation, and bias analysis; LIME- based local interpretability analysis to balance predictive performance, governance reliability, and sustainability objectives. Experimental results demonstrate substantial improvements over conventional approaches using Python. The proposed model achieves better accuracy (98.28%), precision (97.38%), recall (97.48%), F1-score (97.36%), and reduced error rate of 1.63 while enhancing governance-oriented decision intelligence. The findings validate the effectiveness of integrating ethical governance analytics, explainable AI, and DL optimization for developing trustworthy, transparent, and sustainability-driven intelligent systems.