商品描述
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.
商品描述(中文翻譯)
本書討論如何評估、解釋和評價人工智慧（AI）模型和系統的可信度，作者使用基於因果關係的評分方法來衡量對AI模型和工具的信任，特別是在使用AI進行財務決策時。AI系統目前正在大規模部署於實際應用中，因此在使用它們執行關鍵活動之前，定義、衡量和傳達可以指示AI可信度的指標是非常重要的。儘管AI的普及程度不斷增加，但在如何有效評估基於AI的系統以確保其負責任、公正和準確方面仍存在差距。本書提供有關尖端AI可信度的背景資訊，以便做出重要決策，讀者將學習如何在可解釋性、因果關係以及影響可信度的因素（如偏見指標）方面進行系統性思考。其他主題包括遵循監管和市場需求的合規性，以及對AI系統的「信任分數」或「信任評級」概念的探討，這些指標將被審查、增強並應用於多個AI範例。
作者簡介
Kausik Lakkaraju is a doctoral candidate at the AI Institute of the University of South Carolina, specializing in evaluating AI systems through causal analysis. He has worked extensively with AI models spanning unimodal (text, image, and numeric/ time-series) and multimodal diversity in a variety of forms including foundations models, rule-based systems, and chatbots and with applications in finance, health, and education. He has worked closely with researchers at JP Morgan Chase Research leading to a tutorial on AI trustworthiness in finance at ICAIF, and he has received many recognitions including best poster awards. Biplav Srivastava, Ph.D., is a Professor of Computer Science at the AI Institute and Department of Computer Science at the University of South Carolina (USC). With over three decades of AI experience in industry and academia, he directs the 'AI for Society' group, which is investigating how to enable people to make rational decisions despite the real world complexities of poor data, changing goals, and limited resources by augmenting their cognitive limitations with technology. His work in AI spans the sub-fields of reasoning (planning, scheduling), knowledge extraction and representation (ontology, open data), learning (classification, deep, adversarial), and interaction (collaborative assistants), and extends their application for services (process automation, composition) and sustainability (governance - elections, water, traffic, health, power). Dr. Srivastava has been conducting research in trustworthy AI for a decade, introduced the first course on the topic at USC, and has led USC's participation in the National Institute of Standards and Technology (NIST)'s Artificial Intelligence Consortium (AIC) since its inception in 2024.
作者簡介(中文翻譯)
Kausik Lakkaraju 是南卡羅來納大學人工智慧研究所的博士候選人，專注於通過因果分析評估人工智慧系統。他在各種形式的人工智慧模型上有廣泛的工作經驗，包括單模態（文本、圖像和數值/時間序列）和多模態的多樣性，涵蓋基礎模型、基於規則的系統和聊天機器人，並應用於金融、健康和教育等領域。他與摩根大通研究所的研究人員密切合作，導致在 ICAIF 上進行了一個有關金融中人工智慧可信度的教程，並獲得了多項榮譽，包括最佳海報獎。
Biplav Srivastava 博士是南卡羅來納大學（USC）人工智慧研究所及計算機科學系的教授。擁有超過三十年的人工智慧產業和學術經驗，他負責「社會的人工智慧」小組，該小組正在研究如何通過技術增強人們的認知限制，使他們能夠在數據不佳、目標變化和資源有限的現實世界複雜性中做出理性決策。他在人工智慧領域的工作涵蓋推理（規劃、排程）、知識提取與表示（本體、開放數據）、學習（分類、深度、對抗）和互動（協作助手）等子領域，並擴展其在服務（流程自動化、組合）和可持續性（治理 - 選舉、水、交通、健康、電力）方面的應用。Srivastava 博士在可信人工智慧方面進行了十年的研究，並在 USC 開設了該主題的第一門課程，自 2024 年以來，他一直領導 USC 參與國家標準與技術研究所（NIST）的人工智慧聯盟（AIC）。
