Topic

Explainability

Explainability refers to the ability to understand and articulate why an AI system made a specific decision or prediction, making its reasoning transparent to users and stakeholders. In enterprise AI governance, explainability is critical for building trust, meeting regulatory requirements like GDPR and emerging AI regulations, and enabling human oversight of automated decisions that affect customers or employees. Organizations must balance the performance benefits of complex models with the need for interpretable results, particularly in high-stakes domains such as lending, hiring, and healthcare where decision transparency is both a legal and ethical imperative.

1 item

ResearchGlobal2026-07-06

IBM IBV Framework Exposes the Accountability and Auditability Gap Holding Enterprise AI Governance Programs Back

The IBM Institute for Business Value has published 'The Enterprise Guide to AI Governance,' a practitioner-facing report that identifies leadership accountability, multidisciplinary team structures, and explainable and auditable AI outputs as the core implementation challenges facing enterprise compliance programs globally. The report provides structured recommendations for building governance infrastructure capable of supporting both regulatory requirements and responsible human-AI collaboration. It is directed at executives and governance professionals across industries deploying AI at material scale.

IBM leadership accountability explainability auditability AI governance maturity multidisciplinary teams human-AI collaboration enterprise AI governance