Artificial intelligence (AI) has seen tremendous advancements in recent years, and it has become an essential tool in various industries. However, AI systems are often considered a “black box” because it is challenging to understand how they make decisions. Explainable AI (XAI) aims to address this issue by providing explanations for the predictions made by AI models. In this post, we will take a deep dive into the latest developments in XAI and explore some of the most popular interpretability techniques, such as LIME and SHAP.
What is Explainable AI?
Explainable AI, also known as interpretable AI or XAI, is a field that aims to make AI models more transparent and understandable. It is essential to have interpretable models, particularly in applications where human lives are at risk or where the decisions have a significant impact on society. For example, healthcare, finance, and legal systems all require XAI to ensure that the decisions made by AI systems are fair and unbiased.
Why is Explainable AI important?
XAI has become increasingly important as AI is being used in more critical applications. With traditional AI models, it is challenging to understand how they arrived at their predictions, which can lead to mistrust and reluctance to use the model. Furthermore, in industries such as healthcare, finance, and law, it is essential to understand the reasoning behind the model’s predictions to ensure that they are fair and unbiased. XAI also plays a crucial role in identifying and correcting errors made by the model.
There are several interpretability techniques that have been developed to make AI models more transparent. In this section, we will focus on two of the most popular techniques: LIME and SHAP.
Local Interpretable Model-agnostic Explanations (LIME)
LIME is an interpretability technique that is used to explain the predictions of any classifier, regardless of its architecture. It works by approximating the classifier locally around the instance being explained. LIME generates an explanation by fitting a simple interpretable model to the neighborhood of the instance and analyzing the features used by that model.
SHapley Additive exPlanations (SHAP)
SHAP is another interpretability technique that is used to explain the predictions of any classifier. It works by computing the contribution of each feature to the prediction of the instance being explained. SHAP assigns a score to each feature, representing its importance in the prediction, and it guarantees that the sum of the scores for all features equals the prediction.
Explainable AI is an essential field that aims to make AI models more transparent and understandable. With the increasing use of AI in critical applications, it is essential to understand the reasoning behind the model’s predictions. Techniques such as LIME and SHAP have been developed to provide interpretability for any classifier, regardless of its architecture. By understanding the interpretability techniques, we can ensure that the decisions made by AI systems are fair and unbiased, and we can identify and correct errors made by the model.