Corporate ethical and responsible AI (CERAi) and AI trustworthiness: a lifecycle perspective and empirical insights

Purpose This study develops and empirically validates a Corporate Ethical and Responsible AI (CERAi) lifecycle framework. The framework comprises three interdependent stages: design and development, testing and training, and deployment and usage. The study aims to move the research beyond abstract principles and policy analyses toward empirically grounded, lifecycle-integrated artificial intelligence AI governance theory. It positions ethical AI and responsible AI governance as the two constitutive components of…

By the Truvace deskOctober 16, 20262 min read1 sourcePeer-reviewedTRV-2026-1592· cite & verify

In brief

Researchers built a three-stage lifecycle framework for corporate ethical and responsible AI governance, covering design and development, testing and training, and deployment and usage. They surveyed attendees of a U.S. national AI conference in early 2026 and analyzed 143 usable responses with structural equation modeling.

The results suggest organizations seeking trusted AI may need governance that begins at design, not only visible controls at deployment. The evidence is perception-based and drawn from a small, self-selected group of conference attendees, so it does not show that governance causes trustworthy AI behavior.

Main points

The framework treats ethical AI and responsible AI governance as two components of one construct and AI trustworthiness as a distinct downstream outcome.
Design and development governance and deployment and usage governance each had significant positive direct effects on trustworthiness.
Testing and training governance had no significant direct effect on trustworthiness.
A serial mediation path ran from deployment and usage through testing and training to design and development and then to trustworthiness.

The gain

In a survey of AI professionals, corporate ethical AI governance at the design and development stage and at the deployment and usage stage was associated with higher perceived AI trustworthiness.

The rundown

The survey went to 500 attendees of a national AI conference in the United States, producing 143 usable responses from AI professionals in healthcare, financial services, manufacturing, retail and energy. The authors developed and validated multi-item scales for each lifecycle stage.

The framework covers both conventional machine learning models and autonomous AI agents operating in organizational settings. The authors say it aims to move beyond abstract principles and policy analyses toward empirically grounded governance theory.

Testing and training alone did not mediate the link between deployment and usage and trustworthiness. The authors read this as design-stage ethics acting as the mechanism through which later-stage governance relates to perceived trustworthiness, and they state that stakeholders appear to weight visible deployment-stage governance more heavily.