- 面向高风险应用的机器学习(影印版)(英文版)
- - 作者：(美)帕特里克·霍尔//詹姆士·柯蒂斯//帕鲁尔·潘迪|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576612912
  - 出版日期：2024/03/01
  - 页数：438
- 售价：55.2

内容大纲
过去十年人们见证了人工智能和机器学习（AI/ML）技术的广泛应用。然而，由于在广泛实施过程中缺乏监督，导致了一些本可以通过适当的风险管理来避免的事故和有害后果。在我们认识到AI/ML的真正好处之前，从业者必须了解如何降低其风险。
本书描述了负责任的AI方法，这是一种以风险管理、网络安全、数据隐私、应用社会科学方面的最佳实践为基础，用于改进AI/ML技术、业务流程、文化能力的综合性框架。作者Patrick Hall、James Curtis、Parul Pandey为那些希望帮助组织、消费者和公众改善实际AI/ML系统成果的数据科学家创作了这本指南。
作者介绍
目录
Foreword
Preface
Part Ⅰ. Theories and Practical Applications of AI Risk Management
  1.Contemporary Machine Learning Risk Management
    A Snapshot of the Legal and Regulatory Landscape
       The Proposed EU AI Act
       US Federal Laws and Regulations
       State and Municipal Laws
       Basic Product Liability
       Federal Trade Commission Enforcement
     Authoritative Best Practices
     AI Incidents
     Cultural Competencies for Machine Learning Risk Management
       Organizational Accountability
       Culture of Effective Challenge
       Diverse and Experienced Teams
       Drinking Our Own Champagne
       Moving Fast and Breaking Things
     Organizational Processes for Machine Learning Risk Management
       Forecasting Failure Modes
       Model Risk Management Processes
       Beyond Model Risk Management
     Case Study: The Rise and Fall of Zillow's iBuying ~
       Fallout
      Lessons Learned
    Resources
  2.Interpretable and Explainable Machine Learning
   Important Ideas for Interpretability and Explainability
   Explainable Models
      Additive Models
      Decision Trees
      An Ecosystem of Explainable Machine Learning Models
   Post Hoc Explanation
      Feature Attribution and Importance
      Surrogate Models
      Plots of Model Performance
      Cluster Profiling
   Stubborn Difficulties of Post Hoc Explanation in Practice
   Pairing Explainable Models and Post Hoc Explanation
   Case Study: Graded by Algorithm
   Resources
  3.Debugging Machine Learning Systems for Safety and Performance
   Training
      Reproducibility
      Data Quality
      Model Specification for Real-World Outcomes
   Model Debugging
      Software Testing
      Traditional Model Assessment
      Common Machine Learning Bugs

      Residual Analysis
      Sensitivity Analysis
      Benchmark Models
      Remediation: Fixing Bugs
   Deployment
     Domain Safety
     Model Monitoring
   Case Study: Death by Autonomous Vehicle
     Fallout
     An Unprepared Legal System
     Lessons Learned
   Resources
……
  4.Managing Bias in Machine Learning
  5.Security for Machine Learning
Part Ⅱ.Putting AI Risk Management into Action
  6.Explainable Boosting Machines and Explaining XGBoost
  7.Explaining a PyTorch Image Classifier
  8.Selecting and Debugging XGBoost Models
  9.Debugging a PyTorch Image Classifier
  10.Testing and Remediating Bias with XGBoost
  11.Red-Teaming XGBoost
Part Ⅲ.Conclusion
  12.How to Succeed in High-Risk Machine Learning

内容大纲

作者介绍

目录

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