Introduction

Artificial Intelligence has changed many industries, like healthcare and finance, by automating tasks and making decisions. But as AI gets more complex, we need to understand and trust its results. This is where Explainable AI (XAI) comes in. In this blog, we'll talk about Explainable AI, its tools, why it's important, the tools used in the industry, and how it might change the future of AI.

What are Explainable AI Tools? 

Explainable AI tools help us understand AI systems. They check data for biases and fairness, create explanations, and measure their reach. XAI tools help companies understand how AI models make decisions by analysing data, evaluating models, and monitoring production.

Why is Explainable AI Important? 

Explainable AI is essential. It helps us trust AI models and encourages responsible use. XAI lets developers ensure the system works as expected and meets rules and regulations. It also enables people affected by AI decisions to question or change outcomes, making AI processes more transparent and accountable.

Explainable AI Frameworks

There are several frameworks in the industry for making AI systems explainable. Here are a few:

• What-if Tool: This easy-to-use tool, powered by TensorFlow, helps analyse and evaluate AI models. It shows datasets, compares models, and helps us understand them better.
• LIME (Local Interpretable Model-Agnostic Explanations): LIME helps understand complex models by explaining the model's decisions.
• DeepLIFT: This tool focuses on each neuron's activation and gives scores based on comparisons. It uncovers hidden dependencies and calculates scores efficiently, providing insights into model behaviour.
• Skater: Skater helps interpret different models, helping create understandable machine learning systems. It addresses real-time use cases and helps with both global and local understanding.

How Explainable AI Can Change AI in the Future 

Explainable AI could change AI in the future. With XAI, we can expect better collaboration between humans and AI, more reliable models, and more acceptance of AI systems. By explaining AI decisions, XAI helps us understand and trust AI, leading to more use of AI in essential areas like healthcare, finance, and self-driving cars.

Also, XAI tools help with AI ethics and fairness. They identify and reduce biases and unfair patterns, making sure AI systems are transparent and accountable. This means AI can be used more responsibly and inclusively.

Conclusion

Explainable AI could change the AI field. By helping people understand and trust AI results, XAI tools increase transparency, accountability, and ethical considerations. As industries start using XAI, we can expect a move towards more understandable and responsible AI systems.

In the future, Explainable AI will help humans and machines work together better, letting us use AI to its full potential while ensuring its decisions are fair and understandable. As we go forward, using XAI in different sectors will change how we see and interact with AI, making it a vital tool for the future.

Remember, we're just starting on the journey to a future where AI is fully explainable and has a lot of potential for innovation and progress.