Explainable Artificial Intelligence Models for Transparent and Accountable Decision Support Systems

Patrick Suskind Sebald

Explainable Artificial Intelligence Models for Transparent and Accountable Decision Support Systems

Authors

Patrick Suskind Sebald

AI Researcher, Germany.

Keywords:

XAI, Explainable AI, Decision Support Systems, Accountability, Transparency, AI Ethics, Interpretable Machine Learning, Responsible AI

Synopsis

As artificial intelligence (AI) continues to be embedded into critical decision-making infrastructures, the demand for explainability has become imperative. Explainable Artificial Intelligence (XAI) addresses this by enhancing transparency and accountability, particularly in decision support systems (DSS) operating in sensitive sectors like healthcare, finance, and law. This paper evaluates the current state of XAI models, emphasizing their interpretability, ethical implications, and domain-specific applications . Through a review of major pre-2024 literature and visualization of trends, we provide insight into how XAI enables stakeholders to make informed, fair, and accountable decisions.

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