Örgütsel Yapay Zekâ Kararlarının Açıklanabilirlik Yükü ve Hesap Verebilirliği: Blok Zinciri Tabanlı Bir Yönetişim Modeli

Abstract

Kurumsal karar alma alanında yapay zekânın (YZ) hızla yaygınlaşması, hesap verebilirlik krizini keskinleştirmiştir. Finans, sağlık ve insan kaynaklarında YZ sistemleri daha yüksek riskli karar çıktıları üzerinde etkili olurken, paydaşlar kararların nasıl verildiğine dair şeffaflık ve hatalar karşısında daha güçlü hesap verebilirlik talep etmektedir. Ancak açıklanabilirliğe ilişkin yaklaşımlar; silo temelli sorumluluk yapıları, zayıf denetim izleri ve yeterince tanımlanmamış bir “açıklanabilirlik yükü” nedeniyle sınırlı kalmaktadır. Bu yük, YZ kararlarına ilişkin açıklamaları üretme, sürdürme, doğrulama ve güvence altına alma emeğini ifade eder. Bu kavramsal-kuramsal makale, blok zincirini değiştirilemez bir yönetişim altyapısı olarak ele alarak bu yükün paydaşlar (geliştiriciler, veri sağlayıcılar, süreç sahipleri ve denetçiler) arasında nasıl dağıtılabileceğine ilişkin bir çerçeve kurar. Algoritmik hesap verebilirlik, kurumsal kuram ve dağıtık yönetişim literatürüne dayanarak; açıklama beklentilerini risk maruziyeti ve kapasiteye göre nicelleştiren ve tahsisleri akıllı sözleşmeler aracılığıyla uygulanabilir kılan biçimsel bir model önerilmektedir. Model, açıklanabilirlik faaliyetini ölçülebilir bir yük olarak görür ve şeffaflık ile meşruiyeti güvence altına almak için tahsis edilmesi gerektiğini savunur. Blok zinciriyle sağlanan denetlenebilirlik, paydaş güveni, düzenleyici uyum ve örgütsel öğrenme arasındaki ilişkiye dair test edilebilir beş önerme geliştirir; ayrıca AB Yapay Zekâ Tüzüğü (EU AI Act), GDPR Madde 22 ve yaklaşan YZ yönetişimi rejimleri için çıkarımlar sunar ve uygulamaya dönük yön vermektedir.

Keywords

• Açıklanabilir yapay zeka
• Blok zincir
• Algoritmik hesap verebilirlik
• Dağıtık yönetişim
• Sorumluluk tahsisi
• Kurumsal teori

Explainability Burden and Accountability of Organizational AI Decisions: A Blockchain Based Governance Model

Abstract

The speed with which artificial intelligence has proliferated in organizational decision-making has intensified the accountability crisis. In finance, healthcare, and human resources, AI systems increasingly influence high-risk decision outcomes, with stakeholders demanding transparency concerning how decisions are made and accountability for failures. Yet prevailing understandings of explainability are limited by siloed responsibility structures, weak audit trails, and an under-specified “explainability burden”—the labor associated with the production, maintaining, verifying, and assuring of explanations for decisions made by AI. This conceptual article builds a framework for distributing that burden among key stakeholders (AI developers, data providers, process owners, auditors) using blockchain as an immutable governance infrastructure. Building on research in algorithmic accountability, institutional theory and distributed governance, the article proposes a framework for quantifying explanation expectations as a function of risk exposure and capacity and implementing these allocations through smart contracts as smart contracts on blockchain platforms. The model regards explainability as a measurable burden that must be strategically allocated to ensure transparency and legitimacy. I derive five testable propositions linking blockchain-based auditability to accountability outcomes, stakeholder trust, regulatory compliance and organizational learning, and provide insights for organizations dealing with the EU AI Act, GDPR Article 22 and upcoming AI governance regimes.

Keywords

• Explainable AI
• Blockchain
• Algorithmic accountability
• Distributed governance
• Responsibility allocation
• Institutional theory

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