What is Explainable AI? Is AI safe & Explainable?

Artificial Intelligence (AI) is a rapidly growing field that is changing the world as we know it. It is the branch of computer science that deals with the creation of intelligent machines that can perform tasks that typically require human intelligence.

What is Explainable AI? 

Explainable AI (XAI) is a branch of Artificial Intelligence that aims to develop AI systems that can provide clear and transparent explanations for their decisions and predictions. XAI is important because many AI systems, particularly those based on deep learning algorithms, can be seen as “black boxes” that produce decisions that are difficult or impossible for humans to understand.

Explainable AI (XAI) Approaches

Model-agnostic methods provide explanations for the predictions made by any AI model, regardless of its architecture or internal workings. Model-specific methods, on the other hand, are tailored to the inner workings of specific AI models, and provide explanations that are specific to those models.

How Explainable AI (XAI) can be used in practice

Suppose you are a doctor and you want to use an AI model to predict the likelihood of a patient developing a certain medical condition. The model is trained on a large dataset of patient records, and is able to make predictions with high accuracy.

Some of the tools available for XAI include:

1. LIME (Local Interpretable Model-agnostic Explanations) 2. SHAP (SHapley Additive exPlanations) 3. Captum 4. ELI5 (Explain Like I’m 5) 5. TensorFlow Lattice 6. H2O.ai

Explainable AI (XAI) tools uses

1. Debugging and Troubleshooting 2. Improving Model Understanding 3. Enhancing Model Interpretability 4. Identifying Model Bias and Fairness Issues 5. Compliance with Regulations 6. Improving Decision-Making

Limitations of Explainable AI (XAI)

1. Computational Complexity 2. Model Performance Trade-off 3. Lack of Human-Understandable Explanations 4. Difficulty in Defining Explanation Quality 5. Limited Explanation Context 6. Model Bias and Fairness

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