Deploying AI without calibrated trust is a strategic and scientific risk. When models operate as black boxes, organizations face compliance liabilities, adoption failure, and cognitive bias.

Our new edited volume, Trust in Artificial Intelligence: Organizational, Educational, Technical and Societal Perspectives (Springer Nature), provides a cross-disciplinary framework bridging empirical research with operational execution.

• Strategic Governance: Actionable human-in-the-loop models, risk mitigation, and change management for business leaders.
• Technical Integrity: Core architectures for Explainable AI (XAI), algorithmic transparency, and bias reduction.
• Educational & Societal Alignment: AI literacy models and institutional oversight designed for researchers and policymakers.

Trust is not an afterthought – it is the prerequisite for sustainable AI adoption.

The book is available for order now via SpringerLink, with official shipping starting September 23.