Citation :

Milana Chantieva , 2025. "AI Frameworks for Ensuring Transparency in Algorithmic Decision-Making" ESP International Journal of Artificial Intelligence & Data Science [IJAIDS]  Volume 1, Issue 1: 1-6.

Abstract :

Artificial Intelligence (AI) is being rapidly adopted across domains where decisions have direct consequences on individuals and communities. The opaque nature of many algorithmic systems has raised ethical, legal, and societal concerns, prompting a surge in efforts to improve transparency. Transparent AI is not merely a technical goal but a multidimensional necessity encompassing clear communication of decision logic, data provenance, model behavior, and governance structures. This paper offers a comprehensive examination of existing frameworks—including legal mandates like the GDPR and the EU AI Act, technical methodologies such as Explainable AI (XAI) and fairness audits, and organizational practices like AI ethics committees and transparency-by-design initiatives. A key contribution is the proposal of an integrative, multi-dimensional framework that aligns regulatory compliance, technical explainability, and operational accountability. This approach facilitates responsible AI deployment and fosters informed stakeholder engagement throughout the AI lifecycle.With the rapid proliferation of Artificial Intelligence (AI) in critical domains such as healthcare, finance, law enforcement, and employment, the demand for transparency in algorithmic decision-making has intensified. Transparent AI systems are essential for fostering trust, ensuring fairness, and enabling accountability. This paper explores existing and emerging frameworks that aim to ensure transparency in AI systems. We examine regulatory, technical, and organizational strategies for improving algorithmic transparency and evaluate their efficacy and limitations. We also propose a multi-dimensional framework that integrates these strategies to enhance transparency across the AI lifecycle.

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Keywords :

AI Transparency, Algorithmic Decision-Making, Explainable AI, Regulatory Compliance, Ethical AI Frameworks, Accountability, Stakeholder Trust, Bias Mitigation.