Platform and Model Design for Responsible AI Design and Build Resilient, Private, Fair, and Transparent Machine Learning Models /

Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainability Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn risk assessment for machine learning frameworks in...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Kapoor, Amita (Autor), Chatterjee, Sharmistha (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited, 2023.
Edición:1st edition.
Temas:
Artificial intelligence > Moral and ethical aspects.
Artificial intelligence.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Contributors
  • Table of Contents
  • Preface
  • Part 1: Risk Assessment Machine Learning Frameworks in a Global Landscape
  • Chapter 1: Risks and Attacks on ML Models
  • Technical requirements
  • Discovering risk elements
  • Strategy risk
  • Financial risk
  • Technical risk
  • People and processes risk
  • Trust and explainability risk
  • Compliance and regulatory risk
  • Exploring risk mitigation strategies with vision, strategy, planning, and metrics
  • Defining a structured risk identification process
  • Enterprise-wide controls
  • Micro-risk management and the reinforcement of controls
  • Assessing potential impact and loss due to attacks
  • Discovering different types of attacks
  • Data phishing privacy attacks
  • Poisoning attacks
  • Evasion attacks
  • Model stealing/extraction
  • Perturbation attacks
  • Scaffolding attack
  • Model inversion
  • Transfer learning attacks
  • Summary
  • Further reading
  • Chapter 2: The Emergence of Risk-Averse Methodologies and Frameworks
  • Technical requirements
  • Analyzing the threat matrix and defense techniques
  • Researching and planning during the system and model design/architecture phase
  • Model training and development
  • ML model live in production
  • Anonymization and data encryption
  • Data masking
  • Data swapping
  • Data perturbation
  • Data generalization
  • K-anonymity
  • L-diversity
  • T-closeness
  • Pseudonymization
  • Homomorphic encryption
  • Secure Multi-Party Computation (MPC/SMPC)
  • Differential Privacy (DP)
  • Sensitivity
  • Properties of DP
  • Hybrid privacy methods and models
  • Adversarial risk mitigation frameworks
  • Model robustness
  • Summary
  • Further reading
  • Chapter 3: Regulations and Policies Surrounding Trustworthy AI
  • Regulations and enforcements under different authorities
  • Regulations in the European Union
  • Propositions/acts passed by other countries
  • Special regulations for children and minority groups
  • Promoting equality for minority groups
  • Educational initiatives
  • International AI initiatives and cooperative actions
  • Next steps for trustworthy AI
  • Proposed solutions and improvement areas
  • Summary
  • Further reading
  • Part 2: Building Blocks and Patterns for a Next-Generation AI Ecosystem
  • Chapter 4: Privacy Management in Big Data and Model Design Pipelines
  • Technical requirements
  • Designing privacy-proven pipelines
  • Big data pipelines
  • Architecting model design pipelines
  • Incremental/continual ML training and retraining
  • Scaling defense pipelines
  • Enabling differential privacy in scalable architectures
  • Designing secure microservices
  • Vault
  • Cloud security architecture
  • Developing in a sandbox environment
  • Managing secrets in cloud orchestration services
  • Monitoring and threat detection
  • Summary
  • Further reading

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