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Artificial Intelligence Techniques for Improving Decision Accuracy and Reliability

Author(s) Sam Krishna
Country Australia
Abstract The increasing complexity of contemporary digital and organizational environments has created a growing demand for intelligent decision-making systems capable of producing accurate, consistent, and reliable outcomes. Conventional decision-making approaches frequently depend on predefined rules, limited datasets, and human interpretation, which can become insufficient when decisions involve large volumes of heterogeneous and rapidly changing information. Artificial intelligence provides advanced analytical capabilities by identifying patterns, evaluating relationships, generating predictions, and supporting evidence-based decisions. This study examines artificial intelligence techniques for improving decision accuracy and reliability and proposes an integrated conceptual framework for intelligent decision support.
The research adopts a conceptual and descriptive methodology based on secondary scholarly literature related to machine learning, deep learning, natural language processing, predictive analytics, expert systems, explainable artificial intelligence, and intelligent decision support. The proposed framework integrates data acquisition, preprocessing, feature analysis, AI-based prediction, decision evaluation, explainability, human validation, and continuous feedback. The analysis demonstrates that AI techniques can improve decision consistency, predictive accuracy, information processing efficiency, and risk identification across diverse applications. However, challenges involving data quality, algorithmic bias, model interpretability, uncertainty, cybersecurity, and changing data distributions can affect reliability. The study concludes that AI can significantly strengthen decision accuracy when implemented through robust data governance, appropriate model selection, continuous validation, explainable mechanisms, and effective human oversight.
Keywords Artificial Intelligence, Decision Accuracy, Decision Reliability, Machine Learning, Predictive Analytics, Explainable AI, Intelligent Decision Support, Data-Driven Decision-Making.
Field Engineering
Published In Volume 1, Issue 2, March-April 2019
Published On 2019-04-30
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