Explainable Artificial Intelligence (XAI)

Abstract

: Explainable artificial intelligence is often discussed in relation to deep learning and plays an important role in the FAT -- fairness, accountability and transparency -- ML model. XAI is useful for organizations that want to build trust when implementing an AI. XAI can help them understand an AI model's behavior, helping to find potential issues such as AI biases. XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision. XAI helps human users understand the reasoning behind AI and machine learning (ML) algorithms to increase their trust. Machine learning (ML) algorithms used in AI can be categorized as "white-box" or "black-box". White-box models provide results that are understandable to experts in the domain. Black-box models, on the other hand, are extremely hard to explain and can hardly be understood even by domain experts. XAI algorithms follow the three principles of transparency, interpretability, and explainability