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Packt Publishing
Prasanna Vijayanathan
Fairness In Generative AI: A Practical Guide To Benchmarking, Bias Mitigation,
And Responsible AI Engineering
N/A
2026
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Publisher > Packt Publishing
Author > Prasanna Vijayanathan
Title > Fairness In Generative AI: A Practical Guide To Benchmarking,
Bias Mitigation, And Responsible AI Engineering
Issue > N/A
Year > 2026
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ISBN > 9781807304867
Pages > 310
Genre > Generative-Ai
Language > English
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Type > RETAIL
Media > TECH
Format > .epub
Section > eBook
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Published > 2026-08
Release > 2026-09-09
Disks > 04 x 5 MB
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- - -- [ D E S C R I P T I O N ] -- - -
Build responsible AI systems with practical fairness benchmarks. Turn
fairness principles into measurable requirements using metrics, human
review, AI governance, CI gates, and drift monitoring across text, image,
and multimodal AI. Key Features Turn generative AI fairness principles into
testable responsible AI requirements Build benchmarks and metrics for LLMs,
image models, and multimodal generative AI Operationalize AI governance
with human review, CI gates, drift monitoring, and scorecards Apply AI
ethics to identify harms, evaluate trade-offs, and guide model releases
Book Description A child asks an image model for a picture of a soccer
player and gets a boy, every time. Across hiring, lending, education, and
healthcare, fairness in generative AI can affect opportunity, dignity, and
trust. This practical guide shows you how to measure fairness and build
responsible AI practices across LLMs, image models, and multimodal
generative AI systems. Move from AI ethics and fairness principles to
engineering artifacts you can test and defend. Define harms and sensitive
attributes, decide whose experience is in scope, and translate fairness
trade-offs into measurable requirements. Then build a modular benchmarking
framework for your ML and MLOps pipelines using versioned scenario and
prompt libraries, quantitative metrics, qualitative rubrics, and human
review. Put AI governance into practice by publishing actionable scorecards
and dashboards, adding fairness checks to CI and release gates, monitoring
drift, and preparing incident-response playbooks. Keep evaluation
meaningful as models, data, and expectations change. A running soccer
image-generator case connects the concepts to implementation, showing how
responsible AI engineering moves from principles to measurable production
controls. What you will learn Turn fairness principles into testable
requirements for generative AI Map harms to representational, allocative,
and procedural checks Design versioned scenario and prompt libraries that
protect real data Implement fairness metrics for text, image, and
multimodal AI outputs Apply human review with rater training and inter-
rater agreement Publish scorecards and dashboards stakeholders can act on
Gate model releases on fairness and monitor drift in production Run
incident-response playbooks that keep benchmarks credible Who this book is
for This book is for people who build, ship, evaluate, and govern
generative AI. ML and data engineers, application developers shipping LLM
features, and responsible AI, trust-and-safety, and security engineers will
gain hands-on value. Product managers and engineering leaders will develop
a shared language for AI governance, while policymakers, regulators,
researchers, standards contributors, and civil-society advocates will find
structured ways to scrutinize deployed systems. A working grasp of ML
evaluation and Python is all you need.
- - -- [ R E T A I L U R L ] -- - -
https://www.packtpub.com/en-us/product/fairness-in-generative-
ai-9781807304867
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