Introduction to Explainable AI
Consider a model that predicts whether a patient is likely to develop a certain terminal disease, a model that assists in making decisions about whether the person on trial is guilty or not, and a model that assists banks to determine whether to give someone a loan or not. Each of these models makes decisions that can have a profound domino effect on multiple lives (unlike a model used by Netflix that recommends movies to watch). Therefore, it is important that institutions with models employed in decision-making processes can explain the reasoning behind their predictions and decisions. Model explainability, or Explainable AI (XAI), deals with developing algorithms and techniques that allow us to understand and interpret the reasoning behind a model’s predictions and decisions. As we have seen in the preceding examples, XAI is especially important in domains such as healthcare, finance, and criminal justice, as the consequences of model decisions...