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230602 ||| eng |
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|a 9781803249773
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| 050 |
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4 |
|a Q334.7
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| 100 |
1 |
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|a Kapoor, Amita
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| 245 |
0 |
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|a Platform and Model Design for Responsible AI
|h [electronic resource]
|b Design and Build Resilient, Private, Fair, and Transparent Machine Learning Models
|c Amita Kapoor, Sharmistha Chatterjee
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| 250 |
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|a 1st edition
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| 260 |
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|a Birmingham
|b Packt Publishing, Limited
|c 2023
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| 300 |
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|a 516 p.
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| 505 |
0 |
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|a 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
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| 505 |
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|a 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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| 505 |
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|a 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
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| 505 |
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|a 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
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| 505 |
0 |
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|a 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
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| 653 |
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|a Artificial intelligence / fast
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| 653 |
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|a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180
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| 653 |
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|a Artificial intelligence / Moral and ethical aspects
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| 653 |
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|a Intelligence artificielle
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| 653 |
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|a artificial intelligence / aat
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| 653 |
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|a Intelligence artificielle / Aspect moral
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| 653 |
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|a Artificial intelligence / Moral and ethical aspects / fast
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| 700 |
1 |
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|a Chatterjee, Sharmistha
|e author
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Description based upon print version of record. - Chapter 5: ML Pipeline, Model Evaluation, and Handling Uncertainty
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| 776 |
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|z 9781803237077
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| 776 |
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|z 1803237074
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781803237077/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.3
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| 520 |
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|a What you will learn Understand the threats and risks involved in ML models Discover varying levels of risk mitigation strategies and risk tiering tools Apply traditional and deep learning optimization techniques efficiently Build auditable and interpretable ML models and feature stores Understand the concept of uncertainty and explore model explainability tools Develop models for different clouds including AWS, Azure, and GCP Explore ML orchestration tools such as Kubeflow and Vertex AI Incorporate privacy and fairness in ML models from design to deployment Who this book is for This book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem
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| 520 |
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|a 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 a global landscape Discover patterns for next-generation AI ecosystems for successful product design Make explainable predictions for privacy and fairness-enabled ML training Book Description AI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it's necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you'll be able to make existing black box models transparent.
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| 520 |
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|a You'll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You'll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you'll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You'll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics. By the end of this book, you'll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You'll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions.
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