AI Research Summary
Before Human ReviewAbstract-based · Full paper not reviewed · Not an official association position
This is a summary based only on the abstract; the full text was not reviewed. The authors argue that governance should focus on the AI-supported organization’s ‘decision-making’ rather than the AI model itself. They present the Decision Assurance by Design (DABD) framework, which embeds assurance requirements throughout the AI lifecycle and connects principles, controls, evidence, and approval authority. They report deriving five capabilities.
Key Points from the Abstract
- It argues that decision-centered AI governance is more appropriate than a model-centered approach.
- It proposes DABD, linking assurance requirements across the AI lifecycle with organizational capabilities.
- It reports identifying five complementary capabilities.
Relevance to AI Law and Policy
It may be useful for designing trustworthy AI decision governance, internal controls, and audit/accountability allocation in enterprises.
Limitations to Consider
- A conceptual paper; empirical validation is not indicated in the abstract.
- The detailed scope and inclusion criteria of the systematic integrative review are unclear from the abstract alone.
- Because the full text was not reviewed, applicability and effectiveness cannot be confirmed.
Please compare with the original text before citing or using it in your assessment.
Abstract Preview
Artificial intelligence increasingly informs consequential enterprise decisions, yet prevailing governance approaches te…Read more in the abstract and original text Research Topic
Original text and source
Decision Assurance by Design A Capability Framework for Trustworthy AI Governance in the Enterprise
The Korean title on this page was translated by AI.- Journal or publication
- Journal of Business Practice and Academic Research
- Original publisher
- Deepiotics Private Limited
- Database
- Crossref