There have been major advances made in the use of machine learning models in a variety of domains, including natural language processing, generative AI, and autonomous systems, to name just a few. On the other hand, as the complexity and scale of models increase, the visibility of how they work inside decreases, which results in a lack of transparency and accountability. The idea of a model's ability to be explained has recently come to the forefront as an important field of research to address this problem. This article explores the significance of machine learning model explainability, as well as the difficulties associated with it and the solutions that have been devised to improve interpretability. Explainability, which enables model openness and insights that humans can understand, not only builds trust in AI systems but also fosters the responsible adoption of AI in applications that are used in the real world.
Concerns regarding the lack of interpretability in the decision-making processes of artificial intelligence (AI) and machine learning (ML) models have been raised due to the increasing integration of these models in various domains. The ability of artificial intelligence systems to provide insights into their predictions or classifications that are comprehensible to humans is referred to as model explainability. This article explores the significance of explainability in models, its applicability to various fields, and the influence that interpretability has on the reliability of artificial intelligence.
