Top 10 Explainable AI Libraries (Python)

The goal of XAI is to provide meaningful explanations for their actions and decisions

What isXAI?

XAI, Explainable AI refers to Artificial Intelligence (AI) systems that can provide clear and understandable explanations for their decision-making processes and predictions. The goal of XAI is to to provide meaningful explanations for their actions and decisions, which can help increase trust, accountability, and transparency. XAI is not exclusive to just explanation but also to conduct the ML experiments in a way that renders the inference easier to extract and explain for a user.

In practice, XAI can be achieved through various methods, such as using feature importance measures, visualization techniques, or by building models that are inherently interpretable, such as decision trees or linear regression models. The choice of method depends on the type of problem being solved and the desired level of interpretability.

Where is XAI Gaining Traction?

XAI is becoming increasingly important as AI systems are being used in a growing number of applications, including health care, finance, and criminal justice, where the potential impact of AI on people’s lives is high, and where it is crucial to understand why a particular decision was made. These are the fields in which the cost of a wrong decision is high (stakes are high). These are the fields in which the decisions are usually taken after a consensus (Jury), or 2nd…