EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR RELIABLE DECISION-MAKING IN AUTOMATED SYSTEMS

Abstract

In recent years, AI is being leveraged for different applications by automated systems to assist in making decisions on various fields like healthcare, financial sector, cybersecurity, transportation, manufacturing, and public services etc. While contemporary machine learning models can make accurate predictions, several of the sophisticated models are “black boxes” and users are unable to comprehend the very reason behind certain decisions. This opacity can hinder transparency, pose challenges in identifying mistakes or bias, and restrict the appropriate applications of AI in critical areas. Explainable Artificial Intelligence (XAI) is one of the solutions to this problem that provides tools to make AI predictions, recommendations, and decision processes more understandable to humans. This paper discusses the contribution of XAI for increasing the reliability of the automated decision-making systems. It provides information on the key components of transparency, interpretability, and accountability, fairness, error detection, and user trust. Additionally, the study investigates a variety of explainability methods such as feature importance, local explanations, visual interpretation, rule-based interpretations, and model independent approaches. It has been reported that explainability can aid in the improvement of AI decisions and the preparation of better human oversight, where organizations can detect inappropriate or ambiguous AI decisions that can lead to significant consequences. Concurrently, the paper notes that there are issues such as the quality of explanations, difficulty of computation, privacy, security, and trade-offs among model quality, interpretability, and model complexity. The study suggests that explainable AI is a crucial factor in building intelligent, reliable, accountable, transparent, and effective AI-powered systems that can be deployed in realistic environments for decision making.