COGNITIVE-INSPIRED EXPLAINABLE MACHINE LEARNING: ADVANCING HUMAN-CENTRIC, TRUSTWORTHY, AND PREDICTIVE ARTIFICIAL INTELLIGENCE-A COMPREHENSIVE REVIEW

Abstract

Artificial intelligence (AI) and machine learning (ML) have transformed decision-making, automation, and predictive analytics across domains such as healthcare, finance, cybersecurity, education, and industrial systems. However, conventional ML and deep learning models are often criticized for their “black-box” nature, lacking transparency, interpretability, and alignment with human reasoning. These limitations reduce trust, accountability, and adoption in high-stakes applications. To address these challenges, cognitive-inspired explainable machine learning has emerged as an interdisciplinary paradigm integrating principles of human cognition, reasoning, memory, perception, and adaptive learning into intelligent systems. Unlike traditional ML approaches focused primarily on predictive accuracy, cognitive-inspired explainable ML emphasizes human-aligned reasoning, contextual understanding, transparent inference, and ethical responsibility. This review critically examines the theoretical foundations, historical evolution, methodologies, and architectural frameworks of cognitive-inspired explainable machine learning. It discusses cognitive computing principles, explainability techniques, neuro-symbolic intelligence, fairness, bias mitigation, robustness, and privacy-preserving models. The review also explores applications in healthcare diagnostics, precision medicine, pharmaceutical drug discovery, psychiatric disorder prediction, financial forecasting, cybersecurity, intelligent transportation, and smart governance. Comparative analyses of explainability methods and cognitive architectures are presented to identify strengths, limitations, and implementation challenges. Furthermore, the review highlights barriers to adoption, including computational complexity, interpretability-performance trade-offs, legal constraints, model uncertainty, data heterogeneity, and ethical concerns in autonomous decision-making. Future directions involving adaptive cognition, emotionally intelligent AI, continual learning, personalized intelligence, and collaborative human-machine ecosystems are also discussed, positioning cognitive-inspired explainable ML as a key pathway toward transparent, trustworthy, and socially responsible AI systems.