What is Explainable AI (XAI)?

Explainable AI (XAI) refers to methods and techniques in the field of AI that make the decision-making process of AI systems understandable to humans. The goal of XAI is to create AI systems that are transparent, interpretable, and trustworthy, enabling humans to understand, appropriately trust, and effectively manage these systems.

Why is XAI important?

XAI is important because as AI systems become more complex and are used in critical decision-making processes, it becomes essential for humans to understand how these systems are making decisions. This understanding is necessary for validating and debugging AI systems, for ensuring that they align with human values, and for building trust in AI systems.

Moreover, in some domains such as healthcare or finance, being able to explain the decision-making process of an AI system is a legal requirement.

What are the challenges and future directions of XAI?

Developing XAI is challenging because there is often a trade-off between the performance of an AI system and its interpretability. Moreover, different stakeholders may require different types of explanations. Future directions of XAI include developing methods that can provide personalized explanations, improving the transparency of AI systems without compromising their performance, and integrating XAI into the design of AI systems from the outset.