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Fairness in Generative AI. A Practical Guide to Benchmarking, Bias Mitigation, and Responsible AI Engineering Prasanna Vijayanathan, Michael Simpson

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Fairness in Generative AI. A Practical Guide to Benchmarking, Bias Mitigation, and Responsible AI Engineering Prasanna Vijayanathan, Michael Simpson - okladka książki

Fairness in Generative AI. A Practical Guide to Benchmarking, Bias Mitigation, and Responsible AI Engineering Prasanna Vijayanathan, Michael Simpson - okladka książki

Fairness in Generative AI. A Practical Guide to Benchmarking, Bias Mitigation, and Responsible AI Engineering Prasanna Vijayanathan, Michael Simpson - audiobook MP3

Fairness in Generative AI. A Practical Guide to Benchmarking, Bias Mitigation, and Responsible AI Engineering Prasanna Vijayanathan, Michael Simpson - audiobook CD

Autorzy:
Prasanna Vijayanathan, Michael Simpson
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Bądź pierwszym, który oceni tę książkę
Stron:
310
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Do przechowalni

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.

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O autorze książki

Prasanna Vijayanathan is a senior AI and platforms engineer with more than a decade of experience building large scale, low latency systems at companies such as Netflix and LinkedIn, focused on observability, reliability, and data driven decision making. He is a Senior Member of IEEE and co leads Safety by Design for Generative AI, including model deployment guidance for child safety and abuse prevention. Across standards bodies and nonprofits, he works at the intersection of engineering practice, AI fairness, and policy, translating principles into concrete architectures, metrics, and evaluation pipelines teams can ship.

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