Pusulaint I Exploring Explainable AI: Understanding the inner workings of AI models and making their decisions interpretative.
Artificial intelligence (AI) has become an integral part of our lives, influencing various aspects of society. However, one of the key challenges with AI is its inherent lack of transparency. Traditional AI models often make decisions that seem like "black boxes," leaving users and stakeholders puzzled about the reasoning behind those decisions. This is where Explainable AI (XAI) comes into play. In this blog post, we will delve into the concept of Explainable AI, exploring its significance, methods, and the benefits it brings to various domains. 1. The Need for Explainable AI: · Discuss the limitations of traditional AI models in terms of transparency and interoperability. · Highlighting real-world examples where the lack of ex-plainability has raised concerns and created challenges. · Explaining why explaining ability is crucial for building trust, ensuring fairness, and making informed decisions. 2. Methods and Techniques