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...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing Ltd
[2023]
|
| Edición: | 1st ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009742735406719 |
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
- Chapter 5: ML Pipeline, Model Evaluation, and Handling Uncertainty
- Technical requirements
- Understanding different components of ML pipelines
- ML tasks and algorithms
- Uncertainty in ML
- Types of uncertainty
- Quantifying uncertainty
- Uncertainty in regression tasks
- Uncertainty in classification tasks
- Tools for benchmarking and quantifying uncertainty
- The Uncertainty Baselines library
- Keras-Uncertainty
- Robustness metrics
- Summary
- References
- Chapter 6: Hyperparameter Tuning, MLOps, and AutoML
- Technical requirements
- Introduction to AutoML
- Introducing H2O AutoML
- Understanding Amazon SageMaker Autopilot
- The need for MLOps
- TFX - a scalable end-to-end platform for AI/ML workflows
- Understanding Kubeflow
- Katib for hyperparameter tuning
- Vertex AI
- Datasets
- Training and experiments in Vertex AI
- Vertex AI Workbench
- Summary.
- Further reading
- Part 3: Design Patterns for Model Optimization and Life Cycle Management
- Chapter 7: Fairness Notions and Fair Data Generation
- Technical requirements
- Understanding the impact of data on fairness
- Real-world bias examples
- Causes of bias
- Defining fairness
- Types of fairness based on statistical metrics
- Types of fairness based on the metrics of predicted outcomes
- Types of fairness based on similarity-based measures
- Types of fairness based on causal reasoning
- The role of data audits and quality checks in fairness
- Assessing fairness
- Linear regression
- The variance inflation factor
- Mutual information
- Significance tests
- Evaluating group fairness
- Evaluating counterfactual fairness
- Best practices
- Fair synthetic datasets
- MOSTLY AI's self-supervised fair synthetic data generator
- A GAN-based fair synthetic data generator
- Summary
- Further reading
- Chapter 8: Fairness in Model Optimization
- Technical requirements
- The notion of fairness in ML
- Unfairness mitigation methods
- In-processing methods
- Explicit unfairness mitigation
- Fairness constraints for a classification task
- Fairness constraints for a regression task
- Fairness constraints for a clustering task
- Fairness constraints for a reinforcement learning task
- Fairness constraints for recommendation systems
- Challenges of fairness
- Missing sensitive attributes
- Multiple sensitive attributes
- Choice of fairness measurements
- Individual versus group fairness trade-off
- Interpretation and fairness
- Fairness versus model performance
- Limited datasets
- Summary
- Further reading
- Chapter 9: Model Explainability
- Technical requirements
- Introduction to Explainable AI
- Scope of XAI
- Challenges in XAI
- Explain Like I'm Five (ELI5)
- LIME
- SHAP.
- Understanding churn modeling using XAI techniques
- Building a model
- Using ELI5 to understand classifier models
- Hands-on with LIME
- SHAP in action
- CausalNex
- DoWhy for causal inference
- DoWhy in action
- AI Explainability 360 for interpreting models
- Summary
- References
- Chapter 10: Ethics and Model Governance
- Technical requirements
- Model Risk Management (MRM)
- Types of model inventory management
- Cost savings with MRM
- A transformative journey with MRM
- Model risk tiering
- Model risk calibration
- Model version control
- ModelDB
- Weights &
- Biases
- Further reading
- Part 4: Implementing an Organization Strategy, Best Practices, and Use Cases
- Chapter 11: The Ethics of Model Adaptability
- Technical requirements
- Adaptability framework for data and model drift
- Statistical methods
- Statistical process control
- Understanding model explainability during concept drift/calibration
- Explainability and calibration
- Challenges with calibration and fairness
- Summary
- Further reading
- Chapter 12: Building Sustainable Enterprise-Grade AI Platforms
- Technical requirements
- The key to sustainable enterprise-grade AI platforms
- Sustainable solutions with AI as an organizational roadmap
- Organizational standards for sustainable frameworks
- Sustainability practices and metrics across different cloud platforms
- Emission metrics on Google Cloud
- Best practices and strategies for carbon-free energy
- The energy efficiency of data centers
- Carbon emission trackers
- The FL carbon calculator
- Centralized learning carbon emissions calculator
- Adopting sustainable model training and deployment with FL
- CO2e emission metrics
- Comparing emission factors - centralized learning versus FL
- Illustrating how FL works better than centralized learning
- The CO2 footprint of FL.
- How to compensate for equivalent CO2e emissions
- Design patterns of FL-based model training
- Sustainability in model deployments
- Design patterns of FL-based model deployments
- Summary
- Further reading
- Chapter 13: Sustainable Model Life Cycle Management, Feature Stores, and Model Calibration
- Sustainable model development practices
- Organizational standards for sustainable, trustworthy frameworks
- Explainability, privacy, and sustainability in feature stores
- Feature store components and functionalities
- Feature stores for FL
- Exploring model calibration
- Determining whether a model is well calibrated
- Calibration techniques
- Model calibration using scikit-learn
- Building sustainable, adaptable systems
- Concept drift-aware federated averaging (CDA-FedAvg)
- Summary
- Further reading
- Chapter 14: Industry-Wide Use Cases
- Technical requirements
- Building ethical AI solutions across industries
- Biased chatbots
- Ethics in XR/AR/VR
- Use cases in retail
- Privacy in the retail industry
- Fairness in the retail industry
- Interpretability - the role of counterfactuals (CFs)
- Supply chain use cases
- Use cases in BFSI
- Deepfakes
- Use cases in healthcare
- Healthcare system architecture using Google Cloud
- Survival analysis for Responsible AI healthcare applications
- Summary
- Further reading
- Index
- Other Books You May Enjoy.
