Explainability and interpretability in AI : Bridging the gap between accuracy and transparency

With more sophisticated computational models increasingly used in decision-making, the requirement for transparency in models has accelerated. Explainability and interpretability are at the forefront of guaranteeing complex models are comprehensible, trustworthy, and responsibly used. Explainability is concerned with giving insights into how decisions are made, while interpretability guarantees users can understand why a model makes certain predictions. This work takes into account the need to balance precision with intelligibility in a variety of applications, especially in high-stakes domains like medicine, finance, and engineering. A variety of approaches to enhancing interoperability are discussed, and their implications for ethical and regulatory requirements. Bridging the gap between precision and intelligibility will allow us to construct more trustworthy and accountable computational systems that are aligned with human values and expectations.