Introduction to Explainable Artificial Intelligence

Once an AI model is created, we need to know whether the predictions/decisions it is making are correct or not. It’s hard for anyone, even the data scientists or engineers who created this model, to determine what is actually happening inside the AI model and how it is giving a certain output. Due to this, the model can be called a black box.

Explainable Artificial Intelligence (XAI), also known as Interpretable AI, helps us solve this problem. It helps us determine the prediction or decision an AI model makes and allows us to measure the accuracy and fairness of any model. Through XAI, it’s easier for humans to trust AI models deployed in critical domains such as finance, healthcare, etc.

Common approaches in XAI

Various methods can be used to implement XAI in AI models. Some of them are given below:

• Local Interpretable Model-Agnostic Explanations (LIME): In this method, a simple model is created based on our original model. Then, we alter inputs given to our original model and provide them to the simple model. We then compare the outputs of the two models to determine how our model reaches a particular decision.

• Shapley Additive Explanations (SHAP): This method is used to predict the output of AI models that consider multiple features of the given input. This is done by creating inputs with different percentages of the features analyzed by the model and assessing the impact of each feature on the output. Through this, we can determine how various features impact the output given by a model.

• Rule-based methods: As the name suggests, in this method, the output of an AI model is determined by looking at the set of rules it follows to reach that result. Here, rules can be thought of as conditions and their corresponding actions.

These are some of the methods used in explanatory AI to determine the output given by an AI model. Through these methods, we can build trust between human and AI models to make them more transparent. Moreover, these methods can also be used to debug an AI model if it runs into an error or isn’t providing the desired results.