The paradox of explainability vs. performance in high-stakes autonomous AI systems: a systematic review of trade-offs, regulatory gaps, and emerging solutions
Year: 2026
Type: review
Study design: Systematic Review
Abstract: This study systematically examines the inherent trade-offs between explainability and performance in high- stakes autonomous AI systems operating within critical domains such as healthcare, criminal justice, finance, and aviation. Employing a PRISMA protocol-led systematic literature review, 45 peer-reviewed articles were selected through rigorous inclusion and exclusion criteria. The findings reveal a persistent tension: while complex models, especially deep learning architectures, achieve superior predictive accuracy, they often lack interpretability, undermining trust, accountability, and regulatory compliance in sensitive decision-making environments. The study emphasizes that explainability is not a peripheral concern but a foundational requirement in applications with profound ethical, legal, and societal implications. It advocates for the development and adoption of hybrid and context-sensitive AI frameworks that balance predictive performance with interpretability. By addressing the governance, user trust, and technical dimensions of this trade-off, the research offers critical insights for both AI developers and policymakers working toward responsible, transparent, and human-aligned AI deployment.
Source: AI Perspectives & Advances
Authors: Uday Nedunuri, Abhijit Das Gupta, Debashis Guha
Institution: S P Jain School of Global Management
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $1,990
OpenAlex ID: W7128788627