A guide to explainable AI principles | Algolia

Explainable artificial intelligence (XAI) is a crucial development in the field of AI that aims to provide clear explanations for AI systems' decision-making processes and machine learning algorithm outputs. With a market forecast of $21 billion by 2030, XAI technology will be pivotal to bringing transparency to the machinations of computer minds. The four principles of explainable artificial intelligence identified by data science experts at the National Institute of Standards and Technology (NIST) are: providing clear explanations for actions, ensuring understandability and meaningfulness of explanations to humans, maintaining accuracy in explanations, and being aware of limitations and uncertainties. Explainable AI is becoming more important due to increasing adoption of AI across various sectors, the need for transparency in autonomous vehicles' decision-making processes, addressing biases in training data sets, adhering to regulatory requirements such as GDPR, and building trust between users and AI systems.