Explainability in AI and Machine Learning: Bridging the Gap Between Intelligence and Interpretation

Abstract

Aim/Purpose This paper examines the role of explainability in AI and ML across diverse scientific fields, including medicine, psychology, geology, education, and public policy. Background Artificial Intelligence (AI) and Machine Learning (ML) are transforming various scientific disciplines, offering powerful tools for prediction, analysis, and decision-making. However, the complexity of these models often makes them opaque, limiting their utility in high-stakes domains. Methodology We selectively sample pivotal domain-specific and cross-disciplinary literature to isolate, contrast, and critique the socio-technical challenges of Explainable AI across five distinct fields of practice. Contribution This paper contributes awareness to the critical role of explainability of AI as it is getting integrated into every domain of life. Findings Transparency and explainability of AI have different roles in different fields of research. Despite the different roles, explainability is critical to the trust in the results and to enable further scientific advancement. Recommendations for Practitioners Practitioners must reject high-level platitudes and deploy domain-specific operational playbooks – such as embedding visual saliency maps in clinical medicine or physical constraints in geosciences – to anchor AI systems in human-centered accountability. Recommendations for Researchers Researchers should empirically investigate human-grounded evaluation metrics and adversarial vulnerabilities to mapping tools, moving past generic post-hoc explanations toward context-sensitive, contestable architectures. Impact on Society Researchers should empirically investigate human-grounded evaluation metrics and adversarial vulnerabilities to mapping tools, moving past generic post-hoc explanations toward context-sensitive, contestable architectures. Future Research It is important to better understand the technical and theoretical aspects that hinder explainability in general and specifically for each research domain and map how these can be overcome.