12,637 papers · updated 18 Sept 2026livingmeta.ai
← Browse all papers
AI evidence extraction

Governance, Accreditation, Ethics, and Trust in Future AI Systems

Abdulkadir Taşdelen · Turkish Academy of Sciences eBooks · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

6/10
Relevance
1/4
Quality (LMQS)
I
Evidence
0
Citations
0.00
FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.53478/tuba.978-625-6110-86-1.ch22

Methodology & findings

Study design

Conceptual/theoretical analysis integrating existing frameworks (OECD AI Principles, NIST AI Risk Management Framework, and international accreditation practices); no empirical data collection or experiments reported.

Main result

The analysis demonstrates that "contemporary AI governance rests on four core pillars: technological design and lifecycle management; stakeholder roles and institutional responsibility; regulatory and compliance mechanisms; and ethical, value-based principles." The chapter further emphasizes that "trustworthy AI depends on continuous monitoring, transparent decision-making, risk- and opportunity-based regulation, and sustained cooperation among multiple stakeholders."

Reports effect sizes.

Research paradigm

Interpretivist/Argumentative

Author conclusions

The authors conclude that "technical excellence alone is not sufficient to achieve trustworthy AI. Meaningful trust requires transparent and proactive governance, ethical responsibility, the protection of human rights, and inclusive stakeholder participation. This holistic approach provides a solid foundation for ensuring that AI systems develop as fair, accountable, and sustainable technologies that contribute positively to societal well-being."

Risk of bias

Selection bias in framework choice (reliance on specific international standards: OECD and NIST frameworks); potential Western-centric perspective in governance model; no empirical validation of proposed framework; author interpretation bias in synthesizing complex governance literature; no transparency regarding excluded perspectives or alternative governance models

Open questions raised

  • The chapter identifies several emerging areas expected to grow in importance: "Topics such as digital sovereignty, flexible and adaptive regulation, security testing and auditing of large-scale models, public–private collaboration, democratic oversight, and stronger human-centered ethical frameworks are expected to grow in importance."
Data: not_statedCode: not_statedExtracted from: pdfAgreement 78%

Explore related topics

Institutional PolicyAI GovernanceConceptual

Related papers

← Browse all papers