Assessing, Explaining, and Rating AI Systems for Trust
With Applications in Finance
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Beschreibung
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.
In addition, this book:
- Addresses decision-making for evaluating and selecting trustworthy AI systems based on general causality principles
- Presents emerging trends in AI regulation as well as strategies to safely deploy AI applications
- Provides actionable insights for evaluating AI systems without requiring deep technical expertise
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.
Addresses decision-making for evaluating and selecting trustworthy AI systems based on general causality principles Presents emerging trends in AI regulation as well as strategies to safely deploy AI applications Provides actionable insights for evaluating AI systems without requiring deep technical expertise
Autor*in
Kausik Lakkaraju
Themen in »Assessing, Explaining, and Rating AI Systems for Trust«
Trustworthy AI AI Assessment AI Trust Evaluation AI and Finance Causality Based Assessment Causality Based Rating Choosing Robust AI Tools AI Trust Scoring Framework Trust Rating and Ranking Robustness and Accuracy of AI Systems