Governance, Accreditation, Ethics, and Trust in Future AI Systems
Artificial Intelligence and National Technology Reflections, Hüseyin Arslan,Abdulkadir Taşdelen,Elvan Kuzucu Hıdır,Ozan Ahmet Çetin, Editör, Tuba-Turkish Acad Sciences, Ankara, ss.527-556, 2026
- Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
- Basım Tarihi: 2026
- Doi Numarası: 10.53478/tuba.978-625-6110-86-1.ch22
- Yayınevi: Tuba-Turkish Acad Sciences
- Basıldığı Şehir: Ankara
- Sayfa Sayıları: ss.527-556
- Editörler: Hüseyin Arslan,Abdulkadir Taşdelen,Elvan Kuzucu Hıdır,Ozan Ahmet Çetin, Editör
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
The rapid spread of Artificial Intelligence (AI) across domains such as public services, healthcare, finance, and security shows that these technologies are no longer just technical tools. AI systems have become complex socio-technical systems that shape decisions, institutions, and everyday life. As a result, they require effective governance, ethical oversight, accreditation mechanisms, and, critically, public trust. This chapter presents a holistic framework for AI governance that spans the entire lifecycle of AI systems. It highlights the close interdependence between technical robustness, ethical principles, institutional accountability, and societal expectations. Drawing on the OECD AI Principles, the NIST AI Risk Management Framework, and international accreditation practices, the chapter argues that trustworthy AI depends on continuous monitoring, transparent decision-making, risk- and opportunity-based regulation, and sustained cooperation among multiple stakeholders. 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. Within this framework, the chapter identifies key challenges, such as algorithmic uncertainty, poor data quality, model drift, cybersecurity risks, discrimination, and bias, alongside emerging opportunities. It shows how these issues can be addressed through well-designed governance structures, systematic auditing, and ethics-by-design approaches. Looking ahead, the chapter emphasizes that AI governance must adapt to a rapidly evolving landscape. Topics such as digital sovereignty, flexible and adaptive regulation, security testing and auditing of large-scale models, public–private collaboration, democratic oversight, and stronger humancentered ethical frameworks are expected to grow in importance. Together, these priorities offer a strategic roadmap for managing AI in a way that is safe, inclusive, and sustainable. Ultimately, the chapter argues 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