Raising AI

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A01=Chirag Shah
algorithmic bias
Author_Chirag Shah
Category=GLM
Category=UYQ
Chat GPT
collaborative systems
eq_bestseller
eq_computing
eq_isMigrated=1
eq_nobargain
eq_non-fiction
Ethical AI
forthcoming
future of AI
I
information access
intelligence
LaMDA
machine learning
neural network
Robot
science fiction
self-driving cars
Terminator
The Matrix

Product details

  • ISBN 9798216452409
  • Dimensions: 156 x 235mm
  • Publication Date: 01 Oct 2026
  • Publisher: Bloomsbury Publishing Plc
  • Publication City/Country: US
  • Product Form: Hardback
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Most AI books either promise utopia or warn of apocalypse. This one does neither.

We are living through the most consequential technology deployment in human history, yet most of the people making decisions about AI—from judges using it in courtrooms to HR executives screening job candidates—don't actually understand how it works or what could go wrong. AI systems embed the biases, assumptions, and blind spots of their creators, then amplify them at scale across society.

Chirag Shah has spent his career building intelligent systems—and the last decade explaining them to the people who actually use them. In this book, he challenges us—and himself—to think deeper about how AI learns and why that matters for how we build and use these systems.

Through vivid encounters with HAL, the Terminator, and TARS, Shah shows how science fiction has shaped our expectations of AI—for better and worse. Using parenting as a metaphor—from strict boundaries to guided autonomy to eventual partnership—he offers three principles for developing AI responsibly as it evolves from narrow tools toward general intelligence.

Blending science fiction, personal narrative, and frontline experience with decision-makers, Shah delivers something rare: an AI book that educates without lecturing, acknowledges uncertainty without despair, and offers a practical framework for developing AI as it matures.

Whether you're building these systems, deploying them, interacting with them, or simply living with their consequences, Raising AI is essential reading.

Chirag Shah is Professor of Information and Computer Science at the University of Washington, Seattle, where he founded the InfoSeeking Lab and co-founded the Center for Responsibility in AI Systems & Experiences (RAISE). His research focuses on building intelligent systems that are transparent, fair, and accountable.

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