Trust, Transparency, and Accountability in AI: The Role of Explainability
Keywords:
Accountability, Explainable artificial intelligence, Human-centred AI, Local interpretable model-agnostic explanations (LIME), SAPAbstract
As Artificial Intelligence (AI) systems become more sophisticated, the demand for transparency and interpretability grows. This paper explores the emerging domain of Explainable Artificial Intelligence (XAI), highlighting its importance in enhancing trust, accountability, and decision support in AI-driven systems. It examines both symbolic and sub-symbolic models, with a particular focus on deep neural networks and their ability to aggregate and process complex data. Furthermore, this survey reviews recent research efforts in XAI across key sectors such as finance, healthcare, and autonomous systems. The study outlines the advantages of XAI, including improved user trust, reduced cognitive burden, and better management of computational overhead. It also identifies critical gaps in current research, proposing future directions that emphasize human-centered design, human-cognitive alignment, and explainability directives. Ultimately, the paper argues that explainability is essential to the efficacy and ethical deployment of AI-operated decision-making systems.
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