AI systems are increasingly making impactful decisions across diverse sectors, but their complexity often obscures their reasoning processes. This lack of transparency poses risks in critical areas, such as healthcare and finance, where understanding AI decision-making is essential. Explainable AI (XAI) seeks to address this challenge by utilizing various techniques that enhance model interpretability. Methods like SHAP and LIME help clarify which inputs influence AI predictions, thereby fostering trust and compliance with regulations like GDPR. By making AI more interpretable, XAI is pivotal for responsible AI deployment in sensitive applications.
AI decision-making has become integral in various fields but lacks transparency. Explainable AI (XAI) aims to bring clarity and trustworthiness to these systems by enhancing interpretability.
The necessity of Explainable AI (XAI) arises from the need for transparency in AI-driven decisions. XAI employs methods to demystify how models consider inputs for predictions.
Collection
[
|
...
]