Full Description
This book discusses how to assess, explain, and rate the trustworthiness of artificial intelligence (AI) models and systems, and the authors use a causality-based rating approach to measure trust in AI models and tools, especially when using AI to make financial decisions. AI systems are currently being deployed at large scale for practical applications, and it is important to define, measure, and communicate metrics that can indicate the trustworthiness of AI before using them to perform critical activities. Despite their growing prevalence, there is a gap in understanding about how to assess AI-based systems effectively to ensure they are responsible, unbiased, and accurate. This book provides background information on cutting-edge AI trustworthiness to make essential decisions, and readers will learn how to think methodically with respect to explainability, causality, and factors affecting trustworthiness such as bias indication. Additional topics include compliance with regulatory and market demands and an examination of the concept of a "trust score" or "trust rating" for AI systems where these metrics are reviewed, augmented, and applied to multiple AI examples.
Contents
Preface.- AI and Trust for Finance.- White-box and Black-box Rating in Literature.- Data and Methods.- Demonstrating the ARC Tool.- Discussion and Concluding Remarks.
